Skip to main content
Download PDF

LDGIdb: a database of gene interactions inferred from long-range strong linkage disequilibrium between pairs of SNPs

BMC Research Notes volume 5, Article number: 212 (2012) Cite this article

Abstract

Background

Complex human diseases may be associated with many gene interactions. Gene interactions take several different forms and it is difficult to identify all of the interactions that are potentially associated with human diseases. One approach that may fill this knowledge gap is to infer previously unknown gene interactions via identification of non-physical linkages between different mutations (or single nucleotide polymorphisms, SNPs) to avoid hitchhiking effect or lack of recombination. Strong non-physical SNP linkages are considered to be an indication of biological (gene) interactions. These interactions can be physical protein interactions, regulatory interactions, functional compensation/antagonization or many other forms of interactions. Previous studies have shown that mutations in different genes can be linked to the same disorders. Therefore, non-physical SNP linkages, coupled with knowledge of SNP-disease associations may shed more light on the role of gene interactions in human disorders. A user-friendly web resource that integrates information about non-physical SNP linkages, gene annotations, SNP information, and SNP-disease associations may thus be a good reference for biomedical research.

Findings

Here we extracted the SNPs located within the promoter or exonic regions of protein-coding genes from the HapMap database to construct a database named the L inkage-D isequilibrium-based G ene I nteraction d atab ase (LDGIdb). The database stores 646,203 potential human gene interactions, which are potential interactions inferred from SNP pairs that are subject to long-range strong linkage disequilibrium (LD), or non-physical linkages. To minimize the possibility of hitchhiking, SNP pairs inferred to be non-physically linked were required to be located in different chromosomes or in different LD blocks of the same chromosomes. According to the genomic locations of the involved SNPs (i.e., promoter, untranslated region (UTR) and coding region (CDS)), the SNP linkages inferred were categorized into promoter-promoter, promoter-UTR, promoter-CDS, CDS-CDS, CDS-UTR and UTR-UTR linkages. For the CDS-related linkages, the coding SNPs were further classified into nonsynonymous and synonymous variations, which represent potential gene interactions at the protein and RNA level, respectively. The LDGIdb also incorporates human disease-association databases such as Genome-Wide Association Studies (GWAS) and Online Mendelian Inheritance in Man (OMIM), so that the user can search for potential disease-associated SNP linkages. The inferred SNP linkages are also classified in the context of population stratification to provide a resource for investigating potential population-specific gene interactions.

Conclusion

The LDGIdb is a user-friendly resource that integrates non-physical SNP linkages and SNP-disease associations for studies of gene interactions in human diseases. With the help of the LDGIdb, it is plausible to infer population-specific SNP linkages for more focused studies, an avenue that is potentially important for pharmacogenetics. Moreover, by referring to disease-association information such as the GWAS data, the LDGIdb may help identify previously uncharacterized disease-associated gene interactions and potentially lead to new discoveries in studies of human diseases.

Keywords

Gene interaction, SNP, Linkage disequilibrium, Systems biology, Bioinformatics

Background

Gene interactions are usually inferred from biological interactions such as protein-protein interactions (PPIs) [1–3], co-expression of genes [4, 5], co-localization of proteins [6, 7], co-evolution of proteins [8, 9], and shared gene-phenotype associations [10]. Gene interactions that are implicated in human disorders are of particular interest [11]. Recently, it has been proposed that the associations between mutations and human disorders can be evaluated at the systems level [11–13]. This concept is based on observations that mutations in different genes can be linked to the same disorders, and that multiple mutations in the same genes can be associated with different diseases [11]. In other words, a human disorder may be the outcome of a molecular system where mutations in different genes are interconnected via a variety of gene interactions. Single nucleotide polymorphisms (SNPs) are frequently associated with human phenotypes, and SNPs in different genes that are strongly correlated with each other may be important for gene interactions. Therefore, exploring the linkages between SNPs may offer new insights into the biological interactions in the human molecular system. A database that stores information about non-physical SNP linkages and possible SNP-disease associations may be helpful for exploring the role of gene interactions in human disorders.

Here we infer potential gene interactions on the basis of long-range linkage disequilibrium (LRLD) between SNPs. We term these potential interactions “linkage disequilibrium-based gene interactions” (LDGIs), where two genes are considered to be connected if the SNPs located in these two genes are subject to strong linkage disequilibrium (LD; usually measured by r2 or D′[14]). Theoretically, LD should be observed between SNPs that are physically close to each other owing to the hitchhiking effect or lack of recombination [15]. In this study, however, we consider only the SNP pairs (designated as LRLD-SNP pairs) that are subject to strong LD (r2 ≥ 0.8) but are located in different LD blocks (or different chromosomes) to minimize the possibilities of accidentally linked SNPs or physical linkage, and thus increase the probability that the associations between the LRLD-linked SNPs/genes are functionally meaningful. To facilitate research based on these inferred SNP linkages (and potential gene interactions), we constructed a user-friendly database, the LDGIdb, to store the information. The LDGIdb also contains information about disease-associated SNPs/genes, such as the associations identified in genome-wide association studies (GWAS) [16] and those recorded in Online Mendelian Inheritance in Man (OMIM) database [17]. Users can thus search for LDGIs that involve disease-associated SNPs/genes, and identify potentially uncharacterized disease-associated gene interactions for further studies.

Findings

Construction of LDGIs

The data analysis workflow is shown in Figure 1. We first extracted human haplotypes from the HapMap Phase II and III data [18], which were generated using the PHASE software [19]. Only the SNPs that are located within the promoter or exonic regions of protein-coding genes (with reference to the Ensembl annotations [20]) were considered. Note that the promoter regions encompass 2 kb sequences upstream of the transcriptional start sites, and exonic regions include coding regions (CDSs) and untranslated regions (UTRs). In view of population stratification, we clustered the individuals examined in the HapMap Phase II and III projects into subpopulations using the PLINK package (version 1.07) [21] (Table 1). Here we consider only the subpopulations that contain at least 20 individuals. For each subpopulation, we calculated LD scores (i.e., r2 and D′[14]) for all combinations of SNP pairs. Two SNPs were considered to be a long-range LD-linked SNP pair (designated as an “LRLD-SNP pair”) if they satisfied both of the following criteria: (1) to avoid the inclusion of accidentally linked SNPs, an LRLD-SNP pair had to be subject to a strong LD (r2 ≥ 0.8); (2) to minimize the probability of hitchhiking or lack of recombination, the two SNPs had to be located in different chromosomes or be separated by at least one recombination hotspot retrieved from the International HapMap Project. The latter criterion may considerably decrease the probability that the identified LRLD-SNP pairs belong to the same “LD blocks” (or “haplotype blocks”, which represent regions where recombination events occur rarely, and consequently LD is maintained) even if they are located in the same chromosomes. Accordingly, we identified 801,340 LRLD-SNP pairs, which contained 94,876 SNPs (Table 1). Genes connected by these LRLD-SNP pairs were considered human LD-based gene interactions (LDGIs). The LDGIdb is composed of a collective total of about 646,203 gene linkages, which contain 21,240 genes (Table 1). Since population stratification was also considered, the LDGIdb also provides potential population-specific gene interactions, which may be useful for investigations of population-specific traits/diseases.

Figure 1
figure 1

Process of identification of LRLD-SNP pairs and LDGIs.

Full size image
Table 1 Identified LRLD-SNP pairs and LDGIs (with r 2 ≥0.8)
Full size table

Calculation of r2 and D′ values

Let P A and P B be the major allele frequencies at SNP1 and SNP2, respectively. Define P a and P b as the minor allele frequencies at SNP1 and SNP2, respectively. Let P AB be the haplotype frequency of observing both A and B alleles at these two loci. Define D = P AB - P A PB. The LD scores, r2 and D′[14], between SNP1 and SNP2 can be computed by

r 2 = ( P AB − P A P B ) 2 P A ( 1 − P A ) P B ( 1 − P B ) = D 2 P A ( 1 − P A ) P B ( 1 − P B ) , and D ′ = { D min ( P A P B , P a P b ) , if D < 0 ; D min ( P A P b , P a P B ) , if D > 0.
(1)

Data retrieval

HapMap Phase II (release 22) and III (release 2) haplotype data and the corresponding recombination hotspot information were retrieved from the International HapMap Project [22]. The human protein-coding genes were downloaded from the Ensembl genome browser (release 53). The human PPI data (designated as “collected PPIs” in the LDGIdb) were collected from seven experiment-supported PPI databases: HPRD [23], DIP [24], MINT [25], IntAct [26], REACTOME [27], BioGRID [28], and MIPS [29]. The extracted PPI collection included a total of 76,955 interactions. The CRG (Centre for Genomic Regulation) human interactomes (designated as “CRG PPIs” in the LDGIdb) were downloaded from Bossi and Lehners’ study [30], which comprised 80,922 interactions. Human gene co-expression data were downloaded from the TMM database [4], which contained 203,043 high-confidence co-expression links that were observed in at least three microarray data sets. The biological interactions inferred from the above databases (i.e., collected PPIs, CRG PPIs, and co-expression links) were integrated into the LDGIdb for comparison with LDGIs. If an LDGI was not found in any of the databases, it was considered to be a potentially uncharacterized gene interaction. The GWAS [16] data were downloaded on August 23rd, 2011 [31]. For LRLD-linked genes, more detailed information was provided including protein domain descriptions (according to Interpro [32], SMART, and PFAM), KEGG pathways [33], and disease association information (OMIM, HIV interaction, and the Genetic Association Database [34]), which were all downloaded from the DAVID knowledgebase [35].

Web interface

Users can search for LRLD-SNP pairs and LDGIs (which are linked by LRLD-SNP pairs) by setting three adjustable parameters: HapMap data source (Phase II or III), P value for PLINK population clustering (P < 0.01 or P < 0.001), and r2 value for linkage disequilibrium (≥0.8, ≥0.9, or 1) (Figure 2A). Note that we only considered population clusters containing at least 20 individuals (Table 1). Also note that LDLR-SNP pairs with r2 = 1 are subject to a “complete” LD. The LDGIdb supports four types of queries. Users can search for LRLD-SNP pairs/LDGIs by specifying the types of genomic location of LRLD-linked SNPs, SNP ID, gene accession number(s), or genomic coordinates (Figure 2B). GWAS-related LRLD-SNP pairs are also provided (Figure 2C). As shown in Figure 2D, the LRLD-SNP pairs/LDGIs are categorized, according to the types of genomic location of the linked SNPs, into promoter-promoter, promoter-UTR, promoter-CDS, CDS-CDS, CDS-UTR and UTR-UTR interactions. The CDS-related LDGIs are further categorized according to whether the LD-linked SNPs are nonsynonymous or synonymous (Figure 2D). Therefore, the user can choose LRLD-SNP pairs that occur in different genomic regions and that (in the case of coding SNPs) represent changes at the RNA or protein levels (the user can choose more than one type of interaction). The user can further select one or more population of interest to retrieve population-specific LDGIs. The results are downloadable (Figure 2E). For simplicity, the web interface displays only the first 10 records of each query (Figure 2F). The user can find detailed information of allele combinations of LRLD-linked SNPs and genomic regions where the linked SNPs are located in the results (Figure 2G). For the identified LDGIs, the interface also provides human PPI data collected from eight experiment-supported databases (i.e., collected PPIs and CRG PPIs) and high-confidence co-expression interactions for comparison. More detailed information of LDGI genes is also provided, including protein domain annotations, biological pathways, and disease associations.

Figure 2
figure 2

The LDGIdb interface. (A) The three adjustable parameters. Users can search for LRLD-SNP pairs and LDGIs by setting the three adjustable parameters: HapMap Phase (II or III), P value of PLINK population clustering (P < 0.01 or P < 0.001), and r2 value for linkage disequilibrium (≥ 0.8, ≥ 0.9, and 1). (B) Types of queries. Users can query by selecting the genomic types of the LRLD-linked SNP loci (D) and the population of interest (E), SNP accession number, gene accession number, or the coordinates of the genomic region of interest. (C) GWAS-related LRLD-SNP pairs. (F) and (G) are results. Users can download all records by clicking on the button (F). The first 10 records are displayed (G). If the linked SNP(s) is located within alternatively spliced genomic regions or overlapping genes, a LRLD-SNP pair record appears more than once with different genomic types or gene accession numbers in the downloaded file.

Full size image

Discussion and future development

Here we propose a new resource for studies of potential human gene interactions (i.e., LDGIs) based on haplotype data. In LDGIs, the linked genes are located in different chromosomes or LD blocks but are connected by one or more exonic/promoter SNP pairs that are subject to strong linkage disequilibrium (r2 ≥ 0.8, ≥ 0.9, or 1). We suggest that this LRLD approach and the LDGIdb can be potentially applied to the following areas. First, LDGIs may represent potential uncharacterized gene interactions, in which the functional associations between the LDGI genes may not be explicitly indicated in other biological networks. Second, although we constructed the LDGIdb using SNP data in this study, the LRLD approach can actually be expanded to include other types of genomic variants such as copy number variation and insertion/deletion. Third, given enough haplotype information, population-specific LDGIs/LRLD-SNP pairs may be identified for more focused studies, particularly in the field of pharmacogenetics. Fourth, the correlation between the LDGIs/LRLD-SNP pairs and disease-associated SNPs such as those identified in GWAS studies can be explored. For example, SNP rs393152, which is associated with Parkinson’s disease [36], forms an LRLD-SNP pair with rs12185268. Interestingly, rs12185268 was demonstrated to be connected to the same disease [37] two years after the publication (i.e., Ref #36) of the association of rs393152 with the disease. Another example is the LRLD-SNP pair: rs9858542–rs3197999. The two SNPs in this pair were shown to be related, respectively, to the Crohn’s disease [38–41] and the ulcerative colitis [42, 43]. These examples show that two SNPs that are associated with the same (or related) human diseases/traits can be identified by our approach. Moreover, there are also cases in which GWAS SNPs and their LDGI partners are associated with the same (or related) human diseases. For example, the GWAS SNP rs5215 in KCNJ11 is known to be associated with Type II diabetes [44, 45]. This SNP forms an LRLD-SNP pair with rs757110, which is located within the CDS of ABCC8. Mutations and deficiencies in the protein encoded by ABCC8 have been suggested to be associated with hyperinsulinemic hypoglycemia of infancy and non-insulin-dependent diabetes mellitus type II [46, 47]. The above examples suggest that the LRLD-SNP linkages may reflect biological interactions in the human molecular system and have the potential to detect previously uncharacterized gene interactions. As disease-association data accumulate, the LDGIdb may become an increasingly powerful tool by which to identify potentially uncharacterized disease-associated gene interactions, contributing to network-based studies of human diseases. Notably, however, since the majority of HapMap SNPs are relatively common variants, the linkages of rare alleles may not be represented in LDGIdb.

This study actually examined whether observed non-physical SNP linkages occur simply by chance or whether they are biologically meaningful. The above examples suggest that the inferred LDGIs may be functionally relevant. One interesting question is what are the molecular mechanisms underlying the inferred gene interactions. For the CDS-CDS LDGIs that involve only nonsynonymous changes, the functional association is speculated to result from direct or indirect protein-level interactions. Of course, the LDGIs may also represent adventitious linkages or false positives that result from unknown population substructures. Meanwhile, the biological meanings of the LDGIs that involve UTR SNPs (i.e., CDS-UTR and UTR-UTR linkages) or synonymous SNPs (i.e., nonsynonymous-synonymous and synonymous-synonymous linkages) may be more subtle. These potential interactions may be associated with translational regulation. Specifically, 5′UTRs may contain multiple sequence features that are involved in translational regulation, including upstream open reading frames, secondary structures, internal ribosome entry sites, and iron regulatory protein binding sites [48]. The disruption of these functional elements may cause changes in the efficiency of protein translation. On the other hand, 3′UTRs are known to be the major binding target of microRNAs, which can also suppress protein expression [49]. In addition, 3′UTRs may harbor protein-interacting secondary structures or the signals of nonsense-mediated decay or polyadenylation [48], both of which can affect the efficiency of protein translation. Meanwhile, synonymous coding SNPs are known to affect mRNA stability and splicing, leading to changes in the corresponding protein products [50]. Since both the UTR and synonymous SNPs may affect protein abundance, dosage imbalance and unidentified, indirect protein interactions may be possible explanations for the observed linkages.

Availability and requirements

Project name: LDGIdb project

Availability: LDGIdb is freely accessible at http://LDGIdb.genomics.sinica.edu.tw. Operating systems: Platform independent

Programming language: Javascript, CSS, PHP

Other requirements: None

References

  1. Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nature reviews. 2004, 5 (2): 101-113. 10.1038/nrg1272.

    Article  PubMed  CAS  Google Scholar 

  2. Benyamini H, Friedler A: Using peptides to study protein-protein interactions. Future Med Chem. 2010, 2 (6): 989-1003. 10.4155/fmc.10.196.

    Article  PubMed  CAS  Google Scholar 

  3. Khan SH, Ahmad F, Ahmad N, Flynn DC, Kumar R: Protein-protein interactions: principles, techniques, and their potential role in new drug development. J Biomol Struct Dyn. 2011, 28 (6): 929-938. 10.1080/07391102.2011.10508619.

    Article  PubMed  CAS  Google Scholar 

  4. Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P: Coexpression analysis of human genes across many microarray data sets. Genome Res. 2004, 14 (6): 1085-1094. 10.1101/gr.1910904.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  5. Ramani AK, Li Z, Hart GT, Carlson MW, Boutz DR, Marcotte EM: A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol Syst Biol. 2008, 4: 180-

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cooper WN, Hesson LB, Matallanas D, Dallol A, von Kriegsheim A, Ward R, Kolch W, Latif F: RASSF2 associates with and stabilizes the proapoptotic kinase MST2. Oncogene. 2009, 28 (33): 2988-2998. 10.1038/onc.2009.152.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Murphy DM, Buckley PG, Das S, Watters KM, Bryan K, Stallings RL: Co-localization of the oncogenic transcription factor MYCN and the DNA methyl binding protein MeCP2 at genomic sites in neuroblastoma. PLoS One. 2011, 6 (6): e21436-10.1371/journal.pone.0021436.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  8. Tillier ER, Charlebois RL: The human protein coevolution network. Genome Res. 2009, 19 (10): 1861-1871. 10.1101/gr.092452.109.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  9. Zill OA, Scannell D, Teytelman L, Rine J: Co-evolution of transcriptional silencing proteins and the DNA elements specifying their assembly. PLoS Biol. 2010, 8 (11): e1000550-10.1371/journal.pbio.1000550.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  10. Jiang X, Liu B, Jiang J, Zhao H, Fan M, Zhang J, Fan Z, Jiang T: Modularity in the genetic disease-phenotype network. FEBS Lett. 2008, 582 (17): 2549-2554. 10.1016/j.febslet.2008.06.023.

    Article  PubMed  CAS  Google Scholar 

  11. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL: The human disease network. Proc Natl Acad Sci U S A. 2007, 104 (21): 8685-8690. 10.1073/pnas.0701361104.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  12. Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabasi AL: The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci U S A. 2008, 105 (29): 9880-9885. 10.1073/pnas.0802208105.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Park J, Lee DS, Christakis NA, Barabasi AL: The impact of cellular networks on disease comorbidity. Mol Syst Biol. 2009, 5: 262-

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wall JD, Pritchard JK: Haplotype blocks and linkage disequilibrium in the human genome. Nature reviews. 2003, 4 (8): 587-597. 10.1038/nrg1123.

    Article  PubMed  CAS  Google Scholar 

  15. Stephan W, Song YS, Langley CH: The hitchhiking effect on linkage disequilibrium between linked neutral loci. Genetics. 2006, 172 (4): 2647-2663.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. 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 U S A. 2009, 106 (23): 9362-9367. 10.1073/pnas.0903103106.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  17. OMIM.http://omim.org/,

  18. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, et al: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449 (7164): 851-861. 10.1038/nature06258.

    Article  PubMed  CAS  Google Scholar 

  19. Stephens M, Donnelly P: A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003, 73 (5): 1162-1169. 10.1086/379378.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Ensembl genome browser. [http://www.ensembl.org/index.html]

  21. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007, 81 (3): 559-575. 10.1086/519795.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  22. HapMap. [http://hapmap.ncbi.nlm.nih.gov/]

  23. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al: Human protein reference database--2009 update. Nucleic Acids Res. 2009, 37 (Database issue): D767-D772.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  24. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, Eisenberg D: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 2002, 30 (1): 303-305. 10.1093/nar/30.1.303.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a Molecular INTeraction database. FEBS Lett. 2002, 513 (1): 135-140. 10.1016/S0014-5793(01)03293-8.

    Article  PubMed  CAS  Google Scholar 

  26. Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, et al: The IntAct molecular interaction database in 2010. Nucleic Acids Res. 2010, 38 (Database issue): D525-D531.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  27. Vastrik I, D’Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, et al: Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007, 8 (3): R39-10.1186/gb-2007-8-3-r39.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (Database issue): D535-D539.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  29. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stumpflen V, Mewes HW, et al: The MIPS mammalian protein-protein interaction database. Bioinformatics (Oxford, England). 2005, 21 (6): 832-834. 10.1093/bioinformatics/bti115.

    Article  CAS  Google Scholar 

  30. Bossi A, Lehner B: Tissue specificity and the human protein interaction network. Mol Syst Biol. 2009, 5: 260-

    Article  PubMed  PubMed Central  Google Scholar 

  31. GWAS. [http://www.genome.gov/gwastudies/#1]

  32. Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Buillard V, Cerutti L, Copley R, et al: New developments in the InterPro database. Nucleic Acids Res. 2007, 35 (Database issue): D224-D228.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  33. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36 (Database issue): D480-D484.

    PubMed  CAS  PubMed Central  Google Scholar 

  34. Becker KG, Barnes KC, Bright TJ, Wang SA: The genetic association database. Nat Genet. 2004, 36 (5): 431-432. 10.1038/ng0504-431.

    Article  PubMed  CAS  Google Scholar 

  35. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, et al: DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35 (Web Server issue): W169-W175.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Simon-Sanchez J, Schulte C, Bras JM, Sharma M, Gibbs JR, Berg D, Paisan-Ruiz C, Lichtner P, Scholz SW, Hernandez DG, et al: Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat Genet. 2009, 41 (12): 1308-1312. 10.1038/ng.487.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  37. Do CB, Tung JY, Dorfman E, Kiefer AK, Drabant EM, Francke U, Mountain JL, Goldman SM, Tanner CM, Langston JW, et al: Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson’s disease. PLoS Genet. 2011, 7 (6): e1002141-10.1371/journal.pgen.1002141.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007, 447 (7145): 661-678. 10.1038/nature05911.

    Article  Google Scholar 

  39. Parkes M, Barrett JC, Prescott NJ, Tremelling M, Anderson CA, Fisher SA, Roberts RG, Nimmo ER, Cummings FR, Soars D, et al: Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn’s disease susceptibility. Nat Genet. 2007, 39 (7): 830-832. 10.1038/ng2061.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  40. Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, Lees CW, Balschun T, Lee J, Roberts R, et al: Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. 2010, 42 (12): 1118-1125. 10.1038/ng.717.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  41. Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD, Brant SR, Silverberg MS, Taylor KD, Barmada MM, et al: Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet. 2008, 40 (8): 955-962. 10.1038/ng.175.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  42. Barrett JC, Lee JC, Lees CW, Prescott NJ, Anderson CA, Phillips A, Wesley E, Parnell K, Zhang H, Drummond H, et al: Genome-wide association study of ulcerative colitis identifies three new susceptibility loci, including the HNF4A region. Nat Genet. 2009, 41 (12): 1330-1334. 10.1038/ng.483.

    Article  PubMed  CAS  Google Scholar 

  43. McGovern DP, Gardet A, Torkvist L, Goyette P, Essers J, Taylor KD, Neale BM, Ong RT, Lagace C, Li C, et al: Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nat Genet. 2010, 42 (4): 332-337. 10.1038/ng.549.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  44. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G, et al: Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008, 40 (5): 638-645. 10.1038/ng.120.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  45. Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, Park KS, Jang HC: Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia. 2009, 52 (2): 253-261. 10.1007/s00125-008-1196-4.

    Article  PubMed  CAS  Google Scholar 

  46. Mannikko R, Flanagan SE, Sim X, Segal D, Hussain K, Ellard S, Hattersley AT, Ashcroft FM: Mutations of the same conserved glutamate residue in NBD2 of the sulfonylurea receptor 1 subunit of the KATP channel can result in either hyperinsulinism or neonatal diabetes. Diabetes. 2011, 60 (6): 1813-1822. 10.2337/db10-1583.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  47. Zhou K, Bellenguez C, Spencer CC, Bennett AJ, Coleman RL, Tavendale R, Hawley SA, Donnelly LA, Schofield C, Groves CJ, et al: Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet. 2011, 43 (2): 117-120. 10.1038/ng.735.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  48. Chatterjee S, Pal JK: Role of 5′- and 3′-untranslated regions of mRNAs in human diseases. Biol Cell. 2009, 101 (5): 251-262. 10.1042/BC20080104.

    Article  PubMed  CAS  Google Scholar 

  49. Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136 (2): 215-233. 10.1016/j.cell.2009.01.002.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  50. Chamary JV, Parmley JL, Hurst LD: Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet. 2006, 7 (2): 98-108. 10.1038/nrg1770.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We especially thank Shaou-Yen Liu and the GRC Information group for technical assistance and the HapMap III project team for providing information about phasing data. This work was supported by the National Science Council, Taiwan (under grants NSC99-2628-B-001-008-MY3 (to T.-J.C.) and National Health Research Institutes intramural funding (to F.-C.C.)

Author information

Authors and Affiliations

  1. Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan

    Ming-Chih Wang, Yen-Zho Chen & Trees-Juen Chuang

  2. Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, 350, Taiwan

    Feng-Chi Chen

  3. Department of Life Science, National Chiao-Tung University, Hsinchu, 300, Taiwan

    Feng-Chi Chen

  4. Department of Dentistry, China Medical University, Taichung, 404, Taiwan

    Feng-Chi Chen

  5. Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-yi County, 600, Taiwan

    Yao-Ting Huang

Authors
  1. Ming-Chih Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng-Chi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yen-Zho Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yao-Ting Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Trees-Juen Chuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Feng-Chi Chen or Trees-Juen Chuang.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

TJC conceived and designed the study. FCC, YTH and TJC conducted the analyses. MCW and YZC built the web server. FCC and TJC wrote the manuscript. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Wang, MC., Chen, FC., Chen, YZ. et al. LDGIdb: a database of gene interactions inferred from long-range strong linkage disequilibrium between pairs of SNPs. BMC Res Notes 5, 212 (2012). https://doi.org/10.1186/1756-0500-5-212

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1756-0500-5-212

Keywords

  • Gene Interaction
  • Linkage Disequilibrium Block
  • International HapMap Project
  • Hyperinsulinemic Hypoglycemia
  • HapMap Phase

BMC Research Notes

ISSN: 1756-0500

Contact us