Type 2 diabetes genetic association database manually curated for the study design and odds ratio
© Lim et al; licensee BioMed Central Ltd. 2010
Received: 11 August 2010
Accepted: 30 December 2010
Published: 30 December 2010
The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.
The list of publications used in this study was collected from the HuGE Navigator, which is an online database of published genome epidemiology literature. Because type 2 diabetes genetic association database (T2DGADB) aims to provide specialized information on the genetic risk factors involved in the development of type 2 diabetes, 701 of the 1,771 publications in the type 2 Diabetes case-control study for the development of the disease were extracted.
In the database, the association results were grouped as either positive or negative. The gene and SNP names were replaced with gene symbols and rsSNP numbers, the association p-values were determined manually, and the results are displayed by graphs and tables. In addition, the study design in publications, such as the population type and size are described. This database can be used for research purposes, such as an association and functional study of type 2 diabetes related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.
The prevalence of type 2 diabetes has reached epidemic proportions worldwide with the largest increase in Asia, Africa and South America . The incidence of life-threatening complications of diabetes, such as retinopathy, nephropathy and lower-limb amputation, caused by the continued exposure of tissue to the high glucose has increased . Since hyperglycemia can be prevented and reversed significantly by lifestyle changes, including the exercise and nutrition, diabetes risk tests, such as one provided by the American Diabetes Association http://www.diabetes.org, have been used to alarm various high risk groups. However, an increase in the incidence of diabetes has not been stopped over the last decade, highlighting the need for new approaches. According to the World Health Organization (WHO), the number of people with type 2 diabetes worldwide was approximately 170 million and 280 million in 2000 and 2010, respectively, which is expected to increase to 430 million by 2030 [1, 3]
Following the growth of genomics, the disease susceptibility of human genetic variations has been examined to provide a better understanding of the pathophysiology of diabetes. The advances in genotyping technologies have increased the scale and accuracy of genotype data, thereby expanding enormously the number of genetic studies demonstrating a relationship between diseases and genetic variations. Consequently, it is difficult to search the massive number of publications from the text files in PubMed http://www.ncbi.nlm.nih.gov/pubmed, which researchers normally access to obtain information. Moreover, a systematic comparison of published data is not possible without considerable effort. Since association studies may have false positives or true negatives, it is important to compare one study with another before drawing a conclusion as to whether the association is true or not. Furthermore, the genetic effect size expressed by the odds ratio in association analysis is not always easy to find from text files.
With these problems in this field, considerable effort has been made to implement public genetic association databases. There are several genetic association databases, such as Genetic Association Database http://geneticassociationdb.nih.gov, dbGAP http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap from NCBI and Catalog of Published Genome-Wide Association Studies http://www.genome.gov/gwastudies from NHGRI. The last two databases focus on the GWAS data, providing a list of genes and their association data as tables. Therefore, its value to researchers who want to examine the association studies carried out using the candidate gene approach has been reduced. Moreover, specific to diabetes, there are several databases, such as T2D-Db http://t2ddb.ibab.ac.in and T1Dbase http://www.t1dbase.org. These databases deal with genetic association studies as well as more integration resources involving gene expression, pathway and protein-protein interaction.
To provide focused information on a T2D association study, this study designed the T2D Genetic Association Database (T2DGADB). T2DGADB using 701 publications of the T2D study provides genetic association data that was manually curated and searchable. The web-based application displays comprehensive summaries of the published T2D genetic association results for browsing, visualization and mining. In addition, the data was displayed graphically adding convenience to understanding them.
Construction and content
The list of publications used in this study was collected from the HuGE Navigator (version 1.3, http://hugenavigator.net) [8, 9], which is an online database of published genome epidemiology literature. The Type 2 Diabetes Mellitus related articles were searched using the HuGE Navigator Phenopedia , which was developed to search gene-disease association summaries using the disease name as the search item. 1,771 published studies beginning from 2001 to October 5, 2009 were obtained from the search. Among them, only case-control study articles for Type 2 Diabetes (T2D) development were selected in order to exclude the articles relevant to diabetes complications (e.g., nephropathy, retinopathy), haplotype analysis and drug treatment (e.g., sulphonylurea, troglitazone, metformin). T2DGADB aims to provide specialized information on the genetic risk factors involved in the development of Type 2 diabetes. The articles on diabetes complications normally deal with the prognostic process of diabetes, and the articles on drug treatments deal with the pharmacogenetic aspects of diabetes patients. The decisions as to how the data on haplotype analysis would be collected are difficult because T2DGADB focused on information regarding each SNP association including the populations used, odds ratio etc. In this point, haplotype analysis could not be well fitted in the format. The final dataset was 701 publications in the Type 2 Diabetes case-control study for the development of the disease.
To select the 701 T2D development papers, the abstracts of 1,771 articles were downloaded through PubMed to be classified into T2D case-control articles or others. This information along with the title, authors, abstract, journal, online or in print publication date and PubMed ID to hyperlink to PubMed Abstract plus were deposited in the T2D Genetic Association Database. In addition, the full text of the 701 articles were downloaded, if accessible, to obtain all available text, tables, figures and supplemental data from the original articles. 625 of the 701 articles could be accessed but 76 articles could not be downloaded or were not written in English.
Since dbSNP builds and human genome builds were updated frequently from 2001 to 2009, it was important to change the Gene names, SNP positions and SNP rs number. Some articles reported only the SNP position and nucleotide change information (e.g., -4034A > C) instead of SNP ID (rs number), and in a few cases, they used their own SNP ID (e.g., SNP1, UCSNP-44). Old gene names and gene aliases were replaced with the Entrez gene official symbol (e.g., sAC→ADCY10, PC-1→ENPP1, last updated Aug 20, 2009).
The author's own SNP ID or SNP position information was replaced with the rs number using the HuGE Navigator Variant Name Mapper, which is an online tool to map the common variation names and rs numbers of genetic variants. However, it was too limited to find all the SNPs missing their own rs number because only 1,159 genes and 5,646 variants were deposited in the database (Aug 20, 2009). An alternative method was to use other databases, NCBI dbSNP, USCS Genome Browser and UCSC In-Silico PCR tool, based on the information provided by the articles. Some papers reported amino acid change information instead of the SNP ID. In this case, they were changed into rs numbers using SNP GeneView of NCBI dbSNP (e.g., PPARG, Pro12Ala→rs1801282). In some cases, a pair of PCR primer sequences was provided for the SNP. The UCSC In-Silico PCR tool can obtain a PCR product sequence with the primer sequences reported in the paper. The UCSC Genome Browser could confirm whether the locations of the PCR product sequences match the genes or chromosome locations described by the author, and the SNPs within the sequences were selected.
Summary of the Data
SNP association information summarizes the results of an association study for each SNP or variant. It contains variation information, such as position information, common name of SNP as reported by the authors, dbSNP rs number, gene name, allele frequency of the reported allele, which is minor allele or risk allele, odds ratio and 95% confidence interval (CI) and p-value. Information on the covariants was added if the authors reported an adjusted p-value for the confounding factors. The association result was determined to be "associated" if the p-value was <0.05 or the odds ratio and 95% CI were in a suitable range. In some cases, the range of odds ratio and 95% CI was suitable but the association result was not associated because the adjusted p-value was not <0.05. In the case where both results were provided in the article, the adjusted p-value was used to determine the association.
Web implementation and Database design
Figure 1 described the data workflow through T2DGADB. T2DGADB includes three local databases such as T2D genetic association database that summarise 701 publications, gene information database that is obtained from NCBI Entrez Gene database, and T2D related publication abstract database that is obtained from NCBI PubMed database. T2DGADB contains three main web pages such as total T2D gene list page, gene information page summarizing distribution of association results of gene and SNPs, and SNP information page summarizing study design and association results of SNPs. Figure 1 also illustrates that user can get the information from T2D related gene list page to SNP information page, sequentially. Each gene, SNP and publication is hyperlinked to NCBI Entrez gene, dbSNP and PubMed, respectively.
T2D gene information features
SNP association result features
Analysis of T2DGADB data
Comparison of T2D related DB with T2DGADB
Hindorff et al.2
Johnson et al.3
T2D Candidate genes
T2D Candidate SNP Markers
~ 2009. 1
2002. 12 ~
~ 2010. 10
T2D candidate gene approach, GWAS
T2D candidate gene approach, GWAS
T1D candidate gene approach, GWAS
association data format
summary of T2D related genetic association study results(sample size, gene, SNP, MAF, p-value, OR, covariant, etc.)
summary of molecular factors using public databases (EST, Transcripts, Unigene, Homologene, GO, Pathways, Tissue Specific Expression, Protein-Protein interactor, Riskfactors, Complications)
summary of GWAS data (Disease, sample size, gene, SNP, MAF, p-value, OR, platform, CNV, etc.)
summary of GWAS data (Disease, sample size, gene, SNP, MAF, p-value, OR, platform, CNV, etc.)
summary of T1D related molecular factors (T1D susceptibility regions, Genetic data, Microarray data, functional annotation, Network & Pathway), analysis tools
This study implemented a database that stores the results of type 2 diabetes association study and detailed information on the study design, and provided graphs that represent the association results. The goal in developing T2DGADB was to provide researchers with quick and easy access to published T2D genetic association information. Moreover, the data in the database was manually curated by adding accuracy to it in order to help researchers evaluate the T2D association study results that have been published from 2001 to 2009. To use the data in T2DGADB, a scientist can begin with a gene with a published association with T2D, then find positive or negative results of all SNPs studied within the gene, discover OR, 95% CI and the p-value of each SNP through a box plot graph, and finally understand the study design information (e.g., study population, sample size) (Figure 1). This is a unique resource to show both study data on the candidate gene approach and GWAS data graphically for T2D researchers. The data can provide a starting point for a genetic study design, systematic review or reference search as well as produce primary genetic data for constructing a diabetes risk test in the preparation of personalized medicine.
This study is a small step in the preparation of a personalized diagnosis system. Using this data, T2D candidate genes can be selected and their risk be estimated. Eventually, the diabetes genetic association database can be utilized to make a computer program that provides health care providers with the individual susceptibility to diabetes for personalized medicine, and can be expanded to the selection of high risk groups for preventive medicine.
In this study, a user-friend database of T2D genetic association of manually curated information was constructed. This database can be used for research purposes, such as an association and functional study of T2D related genes, and as a primary genetic resource to construct a diabetes risk test in the preparation of personalized medicine in the future.
Availability and requirements
T2DGADB is freely available for academic and commercial users at http://t2db.khu.ac.kr:8080.
This work was supported by a grant from the Graduate Research Scholarship, Kyung Hee University Graduate School, Seoul, Republic of Korea.
- Shaw JE, Sicree RA, Zimmet PZ: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010, 87 (1): 4-14. 10.1016/j.diabres.2009.10.007.View ArticlePubMedGoogle Scholar
- Campbell RK: Type 2 diabetes: where we are today: an overview of disease burden, current treatments, and treatment strategies. J Am Pharm Assoc (2003). 2009, 49 (Suppl 1): S3-9. 10.1331/JAPhA.2009.09077.View ArticleGoogle Scholar
- Wild S, Roglic G, Green A, Sicree R, King H: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004, 27 (5): 1047-1053. 10.2337/diacare.27.5.1047.View ArticlePubMedGoogle Scholar
- Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, Hao L, Kiang A, Paschall J, Phan L: The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007, 39 (10): 1181-1186. 10.1038/ng1007-1181.View ArticlePubMedPubMed CentralGoogle Scholar
- Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009, 106 (23): 9362-9367. 10.1073/pnas.0903103106.View ArticlePubMedPubMed CentralGoogle Scholar
- Agrawal S, Dimitrova N, Nathan P, Udayakumar K, Lakshmi SS, Sriram S, Manjusha N, Sengupta U: T2D-Db: an integrated platform to study the molecular basis of Type 2 diabetes. BMC Genomics. 2008, 9: 320-10.1186/1471-2164-9-320.View ArticlePubMedPubMed CentralGoogle Scholar
- Hulbert EM, Smink LJ, Adlem EC, Allen JE, Burdick DB, Burren OS, Cassen VM, Cavnor CC, Dolman GE, Flamez D: T1DBase: integration and presentation of complex data for type 1 diabetes research. Nucleic Acids Res. 2007, D742-746. 10.1093/nar/gkl933. 35 DatabaseGoogle Scholar
- Yu W, Gwinn M, Clyne M, Yesupriya A, Khoury MJ: A navigator for human genome epidemiology. Nat Genet. 2008, 40 (2): 124-125. 10.1038/ng0208-124.View ArticlePubMedGoogle Scholar
- Yu W, Yesupriya A, Wulf A, Qu J, Khoury MJ, Gwinn M: An open source infrastructure for managing knowledge and finding potential collaborators in a domain-specific subset of PubMed, with an example from human genome epidemiology. BMC Bioinformatics. 2007, 8: 436-10.1186/1471-2105-8-436.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu W, Clyne M, Khoury MJ, Gwinn M: Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics. 2010, 26 (1): 145-146. 10.1093/bioinformatics/btp618.View ArticlePubMedGoogle Scholar
- Kim S, Misra A: SNP genotyping: technologies and biomedical applications. Annu Rev Biomed Eng. 2007, 9: 289-320. 10.1146/annurev.bioeng.9.060906.152037.View ArticlePubMedGoogle Scholar
- Johnson AD, O'Donnell CJ: An open access database of genome-wide association results. BMC Med Genet. 2009, 10: 6-10.1186/1471-2350-10-6.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/10/76/prepub
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