Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(7):882-891; doi:10.1093/bioinformatics/btm013
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A new versatile database created for geneticists and breeders to link molecular and phenotypic data in perennial crops: the AppleBreed DataBase
1Walloon Agricultural Research Centre (CRA-W), Gembloux, Liroux 9, B-5030, Belgium, 2Agroscope Changins-Wädenswil (ACW), Phytopathologie, P.O. Box 185, Schloss CH-8820 Wädenswil, Switzerland, 3Genetics and Horticulture – GenHort, National Institute for Agricultural Research, 15 INRA, BP 60057, F-49071 Beaucouzé Cedex, France and 4Plant Research International (PRI), P.O. Box 16, 6700 AA Wageningen, The Netherlands
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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Objective: AppleBreed DataBase (DB) aims to store genotypic and phenotypic data from multiple pedigree verified plant populations (crosses, breeding selections and commercial cultivars) so that they are easily accessible for geneticists and breeders. It will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and in choosing the best parental cultivars for breeding. It also provides high traceability of data over generations, years and localities. AppleBreed DB could serve as a generic database design for other perennial crops with long economic lifespans, long juvenile periods and clonal propagation.
Results: AppleBreed DB is organized as a relational database. The core element is the GENOTYPE entity, which has two sub-classes at the physical level: TREE and DNA-SAMPLE. This approach facilitates all links between plant material, phenotypic and molecular data. The entities TREE, DNA-SAMPLE, PHENOTYPE and MOLECULAR DATA allow multi-annual observations to be stored as individual samples of individual trees, even if the nature of these observations differs greatly (e.g. molecular data on parts of the apple genome, physico-chemical measurements of fruit quality traits, and evaluation of disease resistance). AppleBreed DB also includes synonyms for cultivars and pedigrees. Finally, it can be loaded and explored through the web, and comes with tools to present basic statistical overviews and with validation procedures for phenotypic and marker data to certify data quality.
AppleBreed DB was developed initially as a tool for scientists involved in apple genetics within the framework of the European project, ‘High-quality Disease Resistance in Apples for Sustainable Agriculture’ (HiDRAS), but it is also applicable to many other perennial crops.
Contact: oger{at}cra.wallonie.be
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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Breeding cultivated plants and, in particular, apple trees (Malus x domestica Borkh.), has always been an important activity at both the amateur and professional level. Compared with annual crops, breeding perennial crops is complex, long-lasting and time consuming due to their long juvenile phase and long economic lifespan. Cultivars of perennial crops are grown across large geographical areas and consequently new cultivars and breeding selections have to be evaluated over many successive years in various localities. The age of the trees has to be taken into account when phenotypically characterizing material because phenotypic characteristics change with age. Other characteristics give perennial crops some advantages in genetic research, such as their suitability for vegetative propagation. This means that many old cultivars and breeding selections still exist, which allows an identical genotype to be tested in various localities and in various years. In addition, the simultaneous presence of various successive generations within a single experiment is possible. All these specific characteristics affect breeding procedures and genetic experiments and put high demands on the storage of phenotypic data.
The demands for the storage of genotyping data is also increasing tremendously due to the pace at which high numbers of PCR-based molecular markers are being developed. Initially, studies on marker-trait associations were limited in size, usually involving just a single cross. The use of a single cross suffices as long as the genetic basis of a trait is extremely simple (only one locus with one + allele). In all other cases, multiple crosses are needed if sound conclusions are to be reached on the number of loci, alleles and mode of action of genes (intra- and/or inter-locus interactions). Studies on multiple crosses therefore demand high quality and good data management facilities.
In the perennial apple crop, a new concept of gene and QTL identification was initiated called Pedigree Genotyping (Van de Weg et al., 2004). This approach aims to identify marker-gene associations, functional allelic diversity and both intra- and inter-locus interactions by the integrated analysis of multiple plant populations (crosses, breeding selections and commercial cultivars) that are genetically related by their pedigree. The European project ‘High-quality Disease Resistance in Apples for Sustainable Agriculture’ (HiDRAS) (Gianfranceschi and Soglio, 2004), was initiated to test the concept. In this study, more than 2000 genotypes are being extensively phenotyped and genotyped, delivering more than 1 million data points. Each phenotypic data point is associated with its own descriptors for tree, year, sample and locality. Each genetic data point is associated with its own descriptors for DNA sample, tree, genotype, marker and map position. To meet the needs for the storage and accessibility of these data, a database was needed. There are already several databases managing both genomic and phenotypic information for the plant kingdom. MaizeGDB database (Lawrence et al., 2004), for instance, is a repository for maize sequence, stock, phenotype, genotypic and karyotypic variation, as well as chromosomal mapping data The GrainGenes database (Matthews et al., 2003) focuses on grasses and cereals storing both genetic and phenotypic information. It holds, amongst others, the genealogy and allelic constitution of markers and genes from 69 632 wheat accessions. Other databases have been developed for managing genome molecular information (Rhee et al., 2003; Schoof et al., 2002) or for storing genes and protein information for Arabidopsis thaliana (ABRC, NASC, MATDB).
All these databases focus on annual plants and most of them manage genomic or phenotypic information separately. None of them allows the management of pluri-annual data on the same individual plants (Reiser et al., 2002; Sakata et al., 2000). As none of the existing public databases were able to support extensive studies on marker-trait associations in pedigreed populations of perennial crops, AppleBreed DB was developed. In the context of database construction, apples could serve as a model for perennial crops. Apples are a woody perennial and have a 3–7 year juvenile phase, which is a significant handicap in combining high fruit quality and durable disease resistance by classical breeding. Apples are self-incompatible due to a gametophytic incompatibility system, and therefore inbreeding methods are not applied (Lespinasse, 1992). Apples are vegetatively propagated, have an economic lifespan of about 15 years during which they produce 13 crops, are economically important and are highly rated among consumers, being ranked third in a fresh fruit consumption survey after banana and citrus (Pollack, 2001). Currently, there are more than 10 000 apple cultivars (Morgan and Richardson, 2002; Way et al., 1991) throughout the world. Nevertheless, world apple production is based on a handful of cultivars that are grown in commercial orchards. The most important commercial cultivars are highly susceptible to the most important apple diseases (scab, powdery mildew and European canker), and most of the resistant cultivars do not yet meet the quality demands of consumers. The most important objective of worldwide apple breeding programmes is therefore to combine high fruit quality with good disease resistance. To achieve this aim, breeders need a better understanding of the genetic basis of fruit quality traits and disease resistance, and to obtain access to molecular markers for the most important genes controlling these traits.
AppleBreed DB supports breeders and geneticists in their genetic studies and in their exploration of germplasm collections. Structured information stored in the database should help them not only to elucidate the genetics of complex traits and to assess marker-trait associations, but also to choose more easily and more quickly the most interesting genitors to cross with (e.g. with good disease resistance, a particular taste, or a skin colour preferred by consumers). In this way, it is expected that breeders will more easily be able to create new cultivars meeting consumer preferences and allowing sustainable production systems. This article describes the database model of AppleBreed DB. AppleBreed DB is sufficiently generic to allow it to be used as a model database for many other perennial crops.
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2 METHODS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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AppleBreed DB is intended to be a functional tool for geneticists and breeders. Its data model combines conceptual and logical data models. The aim is to represent the implementation level of information. The conceptual data model (CDM) was the first step in processing the database design, followed by the logical data model (LDM). The CDM includes the main entities/structures and the relationships among them, without specifying attributes or primary keys. Here, the highest levels in the relationships among the different entities are identified. The CDM is divided into super-classes, classes and sub-classes. By definition, a class includes entities characterized by the same profile (e.g. the class marker includes the identities SSR, RFLP, SCAR and AFLP). A super-class is a generalization of the classes. For example, the molecular markers, alleles and map classes give different information on the genome and can be grouped at a superior level into a super-class called ‘Molecular data’. The same principle is used to define the sub-classes.
The LDM features specify all the entities and links defined in the model, as well as the attributes and the primary and foreign keys (keys identifying the link established between different entities). In line with examples of data models described in the literature and with our objectives, the relational data model was chosen to ensure traceability of the collected data. To facilitate access to the database by users, a web interface had to be developed. Consequently, database management applications were chosen that would be accessible through the web (e.g. MySQL) and preferably based on open source programmes and operating systems (e.g. Linux).
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3 RESULTS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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3.1 Conceptual data model (CDM)
The CDM includes six main super-classes: GENOTYPE, PHENOTYPE DATA, MOLECULAR DATA, GROWTHSITE, ORGANIZATION and REFERENCE. As shown in Figure 1, all entities are structured around the super-class GENOTYPE, which is the core element of the model. It covers all plant material by individual trees and DNA samples which can come from any kind of material (cultivars, breeding selections, segregating populations and gene bank accessions). GENOTYPE is subdivided into three classes: PLANT MATERIAL, PASSPORT and SYNONYMS. PLANT MATERIAL includes the two main sub-classes TREE and DNA-SAMPLE, PASSPORT includes the PEDIGREE and ACCESSION main sub-classes, and SYNONYMS includes the SYNONYM and PATRONYM sub-classes.
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TREE and DNA-SAMPLE hold the identity descriptors for each individual tree, DNA sample and genotype name. TREE also includes descriptors for the precise location where trees were grown (institute, plot, row and position in row) and their origin (origin of bud wood, year of sowing, planting and grafting, and rootstock). DNA-SAMPLE also includes the origin of each sample (tree from which the sample was derived, date of isolation and position on micro-titre plates of the original sample as their sub-samples etc.).
ACCESSION is used to identify and characterize the plant material (cultivars, breeding selection, segregating population and gene bank accession information). PEDIGREE describes the parentage of each accession up to the founder level and therefore facilitates ‘Pedigree Genotyping’, a new pedigree-based approach of QTL identification and allele mining in pedigreed populations (Van de Weg et al., 2004). The class SYNONYMS holds the known synonyms and patronyms of each genotype, and accounts for the most frequently occurring typing errors.
Figure 1 shows the relationships between GENOTYPE and other elements of the database. Each genotype is localized in one or more specific trial plots (GROWTHSITE) and each institution (ORGANIZATION) supervises its trial plots. Genotypes are evaluated for their fruit quality and disease resistance (PHENOTYPE DATA). The procedures and results of the genotype DNA analyses are stored in MOLECULAR DATA. Each genotype listed in the database is referenced according to the literature references (Silbereisen et al., 1996; Smith, 1971) in the REFERENCE super-class. Table 1 summarizes the information included in each super-class and the corresponding main classes.
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Most classes are further divided into one to various generations of sub-classes, until the desired level of detail is reached. All these entities have been converted into tables at the LDM level. A class or sub-class may include one or several tables. The most important tables of the database are listed in Table 2.
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As stated earlier, GENOTYPE holds information that identifies genotypes (names of cultivars, breeding selections, crosses and gene bank accessions characteristics) and the tangible part of the plant material (trees and DNA samples). Phenotypic information concerns fruit quality and disease resistance. Finally, molecular information relates to molecular markers used to construct genetic linkage maps, information on mining allele, loci and pedigree of the allele. Each genotype listed in the database is considered to be a central key for the traceability of the information stored in the AppleBreed DB.
3.2 Logical data model (LDM)
The LDM describes entities defined within each super-class and their relationships with other entities defined above. The database diagrams (see Figs 2–4
) give an external view of the AppleBreed DB data content. The consistency of data is automatically checked by the database management system itself, at a superior level, according to the rules and the relationships defined when the schema is implemented. The LDM is presented in more detail for the super-classes (i) GENOTYPE, (ii) PHENOTYPE and (iii) MOLECULAR DATA, specifying their primary and secondary keys.
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3.2.1 GENOTYPE super-class
In the GENOTYPE super-class (Fig. 2) the GT_TREE table and GT_DNA_SAMPLE table are the most important because they allow the individual genotype for the phenotype assessment and the molecular data analysis to be set up. Because of the high importance of plant material identification and certification for genetic studies, the emphasis was put on tracking and tracing aspects for the definition of the structure of these tables. Their detailed content is presented in Tables 3 and 4. The link between them is made through the TREE_LABEL primary key.
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The GT_ACCESSION table is used to store information that is assigned to an accession when it is entered into a collection. The key element of this table is the accession number, which is unique in the collection. Once assigned, this number can never be reassigned to another accession. The GT_PEDIGREE table allows a user to determine whether a relationship exists between phenotypic characteristics and genomic results from genitors and their progenies.
The high number of synonyms for cultivars is a recurrent problem for breeders, geneticists and managers of gene banks; they impair the efficient management and exploitation of the collections.
This is especially true for old genotypes received or collected in different places and times.
For example, Cox's Orange Pippin has more than 40 synonyms. In addition, very modern cultivars often have both a cultivar and a trademark name. Finally, for widely grown cultivars there are often many mutants, each with its own name. For the old cultivars, there are many sources of synonyms. One is translation or transliteration of original names into local languages. There are also spelling errors due to the ‘appropriation’, over time, of introduced foreign genotypes in local traditions, resulting in new local names adapted to the language or dialect (Oger and Lateur, 2004). This problem can lead to major disappointments. For example, geneticists might believe they are working on different genotypes, but after obtaining their results they realize they are working on the same genotype with different names. The database model addresses this problem by using the SYNONYMS main class. The first appellation found in the literature has, in most cases, to be considered as the patronymic name. This name is filled out in the identifier field PATRONYM_NAME in the GT_PATRONYM table, as displayed in Figure 2, and a link between the patronymic name of a genotype and its synonyms is assured through the PATRONYM_ID.
3.2.2 PHENOTYPE DATA super-class
The PHENOTYPE DATA super-class (Fig. 3) includes two main classes: FRUIT QUALITY and DISEASE RESISTANCE. Each genotype is studied for several traits (Gianfranceschi and Soglio, 2004), such as: fruit external characteristics (shape, ground colour, overall colour, fruit size, etc.), fruit internal quality (sugar content, starch index, acidity, etc.), the sensorial evaluations of expert panels to determine the quality of the fruits (sourness, juiciness, firmness, etc.) and the disease levels under natural conditions in the orchard as well as in specially designed greenhouse tests. Data are encoded for each individual assessment, which can be made for a series of individual apples (e.g. firmness data) for different dates (e.g. 0, 2 and 4 months after harvest), localities and years.
Figure 3 also displays the relationships among the main tables of this super-class as well as the relationships with other tables included in other entities, such as GENOTYPE and GROWTHSITE. With regard to the sensorial, instrumental, external and panel expert observations, a composite primary key identifies each observation. This key includes an identifier for the sample, an identifier for harvest times (a date), an identifier for the applied method of assessment and an identifier for the institution making the observations.
This kind of primary key structure gives each institution the possibility of marking its own samples (there is a unique sample code number for each institution).
Each genotype is linked to fruit quality assessment tables (PH_INSTRUMENTAL_ANALYSIS, PH_SENSORIAL_ANALYSIS, PH_EXPERT_PANEL, PH_SENSORIAL ANALYSIS) by the successive tables GT_TREE PK_TREE_LABEL and the table PH_SAMPLE. The PH_SAMPLE_ID field links all the information from instrumental, sensorial and disease observations to trees, and thereby to genotypes.
Each tree is assessed individually, making it possible to connect phenotypic observations with molecular marker data by means of the genotype. This structure allows users to select, for example, a genotype with fruits that have the same level of sugar content and the same starch index, or are similar or dissimilar for other important characteristics. The primary key for PH_DISEASE_ASSESSMENT (the table is included in the DISEASE RESISTANCE class) is also a composite key. This key includes the identifiers of each individual tree, observation date, disease identifier and observed organ plant, as well as an identifier for the applied method of assessment.
3.2.3 MOLECULAR DATA super-class
The objective of the HiDRAS project is to molecularly characterize all the individuals belonging to a selected pedigree using highly informative markers. Families and their connected progenies are chosen for being representative of apple breeding material and differentiated for fruit quality and disease resistance.
One aspect of the project concerns the development of new highly informative molecular markers to fill the gaps in the available apple linkage maps. The origin of all alleles of each marker/genotype combination is assessed in terms of the alleles of the founding cultivars (identity by descent) by analysing marker data.
The MOLECULAR DATA super-class (Fig. 4) is one of the most important components of the model. Its data describes the genetic constitution of each genotype (allelic composition of molecular markers and major genes) and must allow alleles to be traced over generations. Starting from the genotype, all information is linked in the database as a chain. The molecular information is linked to the genotype by the GT_DNA_SAMPLE table and the DNA_SAMPLE_ID. In the MOLECULAR DATA super-class, the MOL_DNA_LINK_LOCUS and the LOCUS_ID make the link with MOL_LOCUS, MOL_ALLELE, MOL_MAPS and MOL_MARKER tables.
The content of the MOL_LOCUS and MOL_MARKER tables is described in Table 5.
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Due to the links between all the tables, the AppleBreed DB can easily provide input data for QTL software to search for combinations of certain molecular markers and fruit quality traits (e.g. skin colour, shape or global taste).
3.3 Database implementation
AppleBreed DB was implemented within a MySQL database system and a Linux environment. A web interface was developed in PhP language. Figure 5 illustrates the data management system adopted for the submission and validation of data. Users send their data to the database administrator via specific, standardized templates (Excel files). Data quality control involves three steps: (1) the structure of the encoding templates (templates created to collect data include control concerning the allowed numeric values or class evaluations), (2) the quality check by the database manager and (3) the constraints existing in the database structure itself (a journal with the error values is generated). Once these checks are achieved, the results regarding suspicious data are sent back to users for validation. After re-submission, the administrator carries out the transfer and integration of data into the database structure. Finally, users can visualize and upload both the raw and interpreted results by accessing specific web pages. Simple SQL queries allow on-line access to the database through the Internet. Various real-time query tools have been developed, including specific multiple-choice questionnaires for different views of the requested information. Data output formats can be generated ‘à la carte’, making output directly compatible for a wide range of software packages, including packages for QTL mapping.
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4 DISCUSSION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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AppleBreed DB is, as far as the authors know, the first database to store both genetic and phenotypic data up to the level of individual observations. This combination of data makes AppleBreed DB a powerful tool for extensive genetic studies directed at the assessment of marker-trait associations, for candidate gene validation and for allele mining. AppleBreed DB takes into account the particularities of perennials such as: (1) vegetatively propagated, allowing the same genotype to be present at various localities, (2) long juvenile phase, (3) multi-annual crop, (4) long economic lifespan and (5) simultaneous availability of successive generations in the same plot of breeding programs, experimental stations and gene banks. These aims and particularities determined the general structure of the database, and have resulted in a framework quite distinct from models in use for annual crops, such as the ZmDB database (Dong et al., 2002; Du et al., 2003; Gai et al., 2000) or the MaizeGDB database (Lawrence et al., 2004).
AppleBreed DB is built on a relational model. The structure of its conceptual model allows for the flexible addition of new entities. In other words, the AppleBreed DB structure allows data with new characteristics to be easily and quickly integrated into the database, at least as long as the database integrity rules are respected. The ability to encode new data into the database is checked by the database structure itself.
Due to the relational structure of the database, users’ queries are easily handled through SQL requests. Other potential real-time query tools can be easily added, such as specific multiple-choice questionnaires for different views of the requested information. Modules to export data in ‘à la carte’ output formats are also under development, making data directly compatible for a wide range of software packages, including packages for QTL mapping. An interesting point for geneticists and breeders is that it is possible to manage traceability of plant material, a genotype or a family and to follow the parents and their descendants. In addition, the flexibility of the data model makes it possible to adapt this system for other multi-annual botanical species. Unfortunately, one characteristic of relational databases might represent an inconvenience. Direct encoding of results is not allowed, for example, for new genotypes or markers. It is always necessary to insert new data in a particular and logical order and according to a specific and defined format.
AppleBreed DB can store phenotypic data at the level on which they were originally assessed, including at the level of individual samples. In addition, the position of trees in the orchard and the genetic relationships among genotypes are documented. Together, this allows in-depth analysis of the data because experimental design, position effects, genetic relationships and experimental variation can be taken into account.
This not only allows in-depth classical analysis of the phenotypic data itself, such as heritability estimates and the effect of different cultivation practices and environments, but also ensures a high-power detection of marker-trait associations. As it stands AppleBreed DB will be a powerful tool for resolving the genetic base of horticulturally important traits. In addition, it has the potential to support valorization of EST and genome sequencing projects, since its phenotypic and genetic data can be helpful in the identification of the candidate genes validated by geneticists.
Currently, there are various public databases for perennial crops that are related to different aspects of genetics and breeding. The USDA-ARS Germplasm Resources Information Network (GRIN http://www.ars-grin.gov/npgs/) is a database which stores information about clonal germplasm in the USDA system, including various tree species as apples, pears stone fruits, grapes, etc. The Genome Database for Rosaceae (GDR, http://www.mainlab.clemson.edu/gdr/) is a curated and integrated web-based relational database. GDR contains data on physical and linkage maps, annotated EST sequences and all publicly available Rosaceae sequences. Although this database started as a database for Prunus, it is now extending to other families of the Rosaceae. Various databases for the management of genetic resources were created by the European Cooperative Programme for Plant Genetic Resources Networks (ECP/GR). These databases are crop specific and include Apple (http://www.ecpgr.cgiar.org/databases/Crops/Malus.htm [Maggioni et al., 2002]), Pear (http://pyrus.cra.wallonie.be/) and various stone fruits (http://www.bordeaux.inra.fr/urefv/base/). The HiDRAS SSRdb (http://www.hidras.unimi.it/) contains detailed information on more than 300 SSR markers that have been mapped in apple. The AppleBreed DB is currently uploading the HiDRAS data, most of which are likely to become public. All these databases are relational, curated and web based. They are continuously extending in content and functionality. Much synergism could be obtained by tuning into their policies, content and formats, and much added value could be obtained if private databases such as the HortResearch Apple EST Database (Crowhurst et al., 2005) became part of the network.
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5 CONCLUDING REMARKS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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The AppleBreed DB model provides a unique tool specifically adapted for geneticists and breeders working on perennial crops with a long economic lifespan, especially when the aim is to combine phenotypic and molecular marker data. It supports pedigree-based analysis of the data, including ‘Pedigree Genotyping’ (Van de Weg et al., 2004). This database could be useful in intercontinental collaboration on marker-trait associations, validation of candidate genes and functional allelic diversity. It can be directly applied to apple, and its structure forms a firm foundation on which other users can build their own applications. It can be easily extended to include various crops, thus forming a base for a RosaceaeBreed DB. Links to other databases such as GRIN, NCBI (National Center for Biotechnology Information), SINGER (System-wide Information Network for Genetic Resources) and EMBL (Nucleotide Sequence Database), can also be investigated.
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ACKNOWLEDGEMENTS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS4 DISCUSSION5 CONCLUDING REMARKSACKNOWLEDGEMENTSREFERENCES |
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This research was carried out with financial support from the Commission of the European Communities (Contract No. QLK5-CT-2002-01492), Directorate-General Research—Quality of Life and Management of Living Resources Program. This manuscript does not necessarily reflect the Commission's views and in no way anticipates its future policy in this area. Its content is the sole responsibility of the authors. The authors are deeply indebted to all participants of the HiDRAS project for their involvement, collaboration and support in the development of the conceptual data model of AppleBreed DB. Funding to pay the Open Access publication charges was provided by Agricultural Walloon Research Centre of Gembloux (Belgium).
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Chris Stoeckert
Received on November 20, 2006; revised on January 11, 2007; accepted on January 12, 2007
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REFERENCES |
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