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Bioinformatics Advance Access originally published online on June 28, 2005
Bioinformatics 2005 21(16):3443-3444; doi:10.1093/bioinformatics/bti557
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

PhD: a web database application for phenotype data management

J.-L. Li 1,2,*, M.-X. Li 1, H.-Y. Deng 3, P. E. Duffy 2 and H.-W. Deng 1,3,4

1Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University Changsha, Hunan 410081, People's Republic of China
2Seattle Biomedical Research Institute 307 Westlake Avenue N, Suite 500, Seattle, WA 98109-5219, USA
3Osteoporosis Research Center, Creighton University Medical Center Omaha, NE 68131, USA
4The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University Xi'an 710049, People's Republic of China

*To whom correspondence should be addressed.


   Abstract
TOP
 Abstract
INTRODUCTION
 IMPLEMENTATION
 REFERENCES
 

Summary: A database application has been developed for phenotype data management employing the Entity-Attribute-Value (EAV) model. By applying the EAV model, this application allows users to manage arbitrary phenotypes and customize data entry forms; therefore, it is suitable for different and multi-center projects.

Availability: http://apps.sbri.org/gpdb (Beta version).

Contacts: Jinlong.Li{at}sbri.org

Supplementary information: http://apps.sbri.org/gpdb/supp.htm


   INTRODUCTION
TOP
 Abstract
 INTRODUCTION
IMPLEMENTATION
 REFERENCES
 
In genetics and genomics studies of complex human diseases large numbers of data points are commonly acquired. Efficient and effective management of the vast amount of data is then necessary. We developed a genotype database management system (DBMS), GenoDB, for large-scale management of the genotype data derived from fluorescently labeled short tandem repeat microsatellite markers (Li et al., 2001). Recently, we developed another DBMS, SNPP, for efficient and convenient management of the large-scale single nucleotide polymorphism (SNP) data (Zhao et al., 2004). However, both GenoDB and SNPP lack functions to manage the phenotype data, which are essential in genetics and genomics studies for complex human diseases.

Phenotypes are heterogeneous for projects that have different aims. For instance, to search genes underlying osteoporosis, researches may focus on bone mass, while to search genes underlying obesity, phenotypes such as body mass index and percentage fat mass may be measured. In addition, data for some phenotype may often be sparse (not uniformly measured or even missing) for some subjects. One reason is that it is unnecessary to collect the phenotype data from all study subjects for some seemingly ‘non-essential’ or unnecessary phenotypes. In addition, it may not be feasible to collect all of the phenotype data from all of the study subjects (missing data).

Instead of the conventional Entity-Relation (E-R) database model, the Entity-Attribute-Value (EAV) model, which conceptually involves tables with three columns (an entity identification, an attribute and the value for that attribute), is suitable for heterogeneous and sparse data. It has been successfully applied to build database applications to manage clinical data that are heterogeneous and sparse (Nadkarni et al., 1999). Although the domain of the phenotype data intersects with that of the clinical data, we should not simply adapt the clinical database application for phenotype data management. Because scientists often study phenotype, genotype and environmental data together for genetics and genomics research, it is necessary to develop a phenotype DBMS that interacts with the genotype DBMS. Here, we present a phenotype DBMS (PhD), developed using the EAV database model, to complement our earlier developed genotype DBMS for microsatellite (Li et al., 2001) and SNP markers (Zhao et al., 2004).


   IMPLEMENTATION
TOP
 Abstract
 INTRODUCTION
 IMPLEMENTATION
REFERENCES
 
Architecture
The server side of PhD includes a MySQL database server and a Sun Java System Application Server. The software packages for installing the servers are freely available from http://www.mysql.com/ and http://java.sun.com/.

Database design
In the EAV design, attributes with different data types (such as integer, real and string) are separated into different tables. Therefore, each EAV table is a data type specific table. An ‘EAV’ database may also use conventional tables when necessary and convenient (Nadkarni et al., 1999). Some tables in PhD are more suitable for the E-R design; therefore, they follow E-R design rules (Fig. 1).



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  		Fig. 1 Database schema of PhD. The top section within the dashed rectangle represents the EAV design, while the bottom one represents the ER design. An entity is represented as a solid rectangle with two sections. Field(s) in the top section of the rectangle denotes primary key(s). FK denotes the foreign key.

 
Functionality
Defining, updating or deleting phenotypes
By employing the EAV design, PhD allows users to manage phenotypes without predefining them in the database at the time of development. This function is particularly important for the development of a general application for different projects and multi-center projects in which phenotypes may vary.

Importing phenotype data into PhD from other sources
Phenotype data can be imported into PhD from MS Excel or text delimited files. If traits in the file have all been defined in the PhD, phenotype data can be directly imported into the database. Otherwise, new traits have to be defined prior to the data importing.

Customizing data entry forms (DEFs) for different projects and users
One important function of PhD is that it lets users create customized DEFs and modify DEFs. This function makes PhD suitable for different and multi-center projects because DEFs are usually varied from project to project. It is also desirable even for a single project when the project evolves and the needs arise for modifying previous DEFs.

Querying, compiling and exporting phenotype data for analysis
PhD provides a function for users to query the database and export the query results for analysis. In addition, because scientists often study phenotype and genotype data together, PhD has been integrated with GenoDB (Li et al., 2001). In this manner, phenotype data can be queried and compiled jointly with genotype and pedigree data for further data analysis or for feeding some popular linkage analysis software directly.


   Acknowledgments
 
The authors would like to thank Tom Blackwell, Jake Masters and Jason Blue for their assistance in setting up the computing environment to host PhD at the Seattle Biomedical Research Institute. H.W.D. and H.Y.D. were partially supported by grants from NIH.

Conflict of Interest: none declared.

Received on May 19, 2005; revised on June 22, 2005; accepted on June 27, 2005

   REFERENCES
TOP
 Abstract
 INTRODUCTION
 IMPLEMENTATION
 REFERENCES
 

    Li, J.L., et al. (2001) Toward high-throughput genotyping: dynamic and automatic software for manipulating large-scale genotype data using fluorescently labeled dinucleotide markers. Genome Res., 11, 1304–1314[Abstract/Free Full Text].

    Nadkarni, P.M., et al. (1999) Organization of heterogeneous scientific data using the EAV/CR representation. J. Am. Med. Inform. Assoc., 6, 478–493[Abstract/Free Full Text].

    Zhao, L.J., et al. (2004) SNPP: automating large scale SNP genotype data management. Bioinformatics, 20, 1–3[Abstract/Free Full Text].


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This Article
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