Nucleic Acids Research Advance Access originally published online on May 3, 2007
Nucleic Acids Research 2007 35(Web Server issue):W212-W216; doi:10.1093/nar/gkm223
Nucleic Acids Research, 2007, Vol. 35, No. suppl_2 W212-W216
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Update of the G2D tool for prioritization of gene candidates to inherited diseases
Carolina Perez-Iratxeta1,*,
Peer Bork2 and
Miguel A. Andrade-Navarro1,3
1Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth, Ottawa, ON, Canada K1H 8L6, 2European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany and 3Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Canada
*To whom correspondence should be addressed. Tel: +1 613 737 8899 Ext 73255; Fax: +1 613 739 6294; Email: cperez-iratxeta{at}ohri.ca
Received January 29, 2007. Revised March 20, 2007. Accepted March 28, 2007.
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ABSTRACT
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G2D (genes to diseases) is a web resource for prioritizing genes
as candidates for inherited diseases. It uses three algorithms
based on different prioritization strategies. The input to the
server is the genomic region where the user is looking for the
disease-causing mutation, plus an additional piece of information
depending on the algorithm used. This information can either
be the disease phenotype (described as an online Mendelian inheritance
in man (OMIM) identifier), one or several genes known or suspected
to be associated with the disease (defined by their Entrez Gene
identifiers), or a second genomic region that has been linked
as well to the disease. In the latter case, the tool uses known
or predicted interactions between genes in the two regions extracted
from the STRING database. The output in every case is an ordered
list of candidate genes in the region of interest. For the first
two of the three methods, the candidate genes are first retrieved
through sequence homology search, then scored accordingly to
the corresponding method. This means that some of them will
correspond to well-known characterized genes, and others will
overlap with predicted genes, thus providing a wider analysis.
G2D is publicly available at
http://www.ogic.ca/projects/g2d_2/
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BACKGROUND
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In the effort to identify which gene or genes produce what disease
phenotype, geneticists are producing an increasing number of
genome-wide linkage analyses and gene association studies that
are generating too many to test candidate genes. For this reason,
during the past five years, the problem of automating the prioritization
of candidate genes to inherited diseases has received increasing
attention from the bioinformatics community. Computational approaches
were made possible due to the availability of the complete human
genome sequence and to considerable developments on database
annotation and data integration for molecular biology databases.
As a result, a number of methods that address this problem have
been published (
1–12). These methods apply a variety of
approaches exploiting known or deduced pieces of information
that range from using only the genomic sequence of the target
region to data mining analyses that include literature and different
annotation systems. For details about these methods see the
Introduction sections of (
13) and (
9). A recent work where many
of these methods were applied to the prioritization of gene
candidates for obesity and type 2 diabetes, shows that there
are many discrepancies between the produced candidate gene lists
(
13). This study suggests the concurrent use of as many approaches
as possible when working with particular diseases, especially
as there are large differences in the kind and amount of useful
information that it is available for each disease. Following
this idea, we are developing and maintaining G2D (genes to diseases),
a resource that seeks to encompass an increasing number of methodologies
to obtain disease-related genes.
G2D is a web resource for prioritizing genes as candidates to inherited diseases, which started as a public server in 2002. At the time, we provided the users with pre-analyses of hundreds of inherited diseases by applying a literature and data mining algorithm based on fuzzy binary relations (2) using the disease's phenotype as input. In 2005, we made the algorithm available as a web tool (14). Now, we have included two more ways of prioritization that apply other principles. One is the use of genes in a different genomic location already known or suspected to produce a similar variant of the disease of interest. The expectation is that the disease-responsible gene will have a similar function to those. This is a more stringent approximation and requires more information about the disease, but it is obviously correct in some cases. The SUSPECTS method (7) has already exploited the idea of gene similarity. The second approach added to G2D, uses as input a second locus where the phenotype has also been mapped in addition to the one where the user is looking for the mutation (as in complex diseases). The expectation is that the products of the genes in several of the loci may be functionally associated, e.g. by participating in the same pathway, or even by being part of a protein complex (9,11). The system will prioritize the candidates according to whether functional interactions (both known and predicted) between proteins encoded in genes of both loci are detected. We used the human protein interactions from the STRING database (15) as our source database.
Besides this web implementation, G2D has been applied by us to the prioritization of genes for complex diseases in two different studies so far: the aforementioned collective computational study to suggest candidates for obesity and type 2 diabetes (13), and to the selection of asthma candidates for genotyping in two linkage regions in a French Canadian founder population (manuscript in preparation).
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OUTLINE OF THE G2D SERVER
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Given a chromosomal region where the user is looking for candidate
genes, there are three ways of using the G2D server depending
on the algorithm that will be applied. The first option takes
as input the phenotype of the disease of interest described
by means of an online Mendelian inferitance in man (OMIM) disease
entry (
16). The system will prioritize the genes according to
the description of the phenotype as provided by the MeSH disease
annotations (
http://www.nlm.nih.gov/mesh/) to the linked bibliography
in the corresponding OMIM entry. For details of this method
see (
2). A second option is to input one or several human or
mouse genes that are already known or are suspected to be involved
in the disease. The system will prioritize the genes in the
target region according to their similarity to the known gene(s)
as given by their GO annotations and high sequence homology.
In order to do this, we compute scores for all GO terms according
to their similarity to the GO terms of the known genes (measured
in terms of their Resnik similarity). The measure favors rare
GO annotations. Next, we apply this GO scoring system to the
annotated genes in Entrez Gene. Their corresponding RefSeq proteins
are compared through BLASTX to the chromosomal target region,
and the scoring is transferred to the hits that are below the
selected E-value. The third option can be used when another
chromosomal region has been also linked to the disease of interest.
The system will look for protein–protein interactions
in the STRING database, both known and predicted that may be
occurring between a gene in the region of interest and a gene
in the second region. Since many interactions in STRING are
predicted, every interaction has associated a score (STRING
score) that reflects how likely is to be a true interaction.
We use the STRING score to sort the candidates within the region
of interest. Candidates that are more likely to interact with
a gene in the second region are then prioritized. In our benchmark
(see Conclusion and Supplementary page in our web site), we
observed higher precision when STRING scores are very high.
Additionally, it is possible to access from our server a database
of pre-calculated results for more than 550 monogenic diseases
on published linkage regions using the phenotype method.
In the next sections, we describe the different use options through examples. We have prepared step-by-step web tutorials that contain very detailed information. They can be accessed from our web server page (see Tutorial section at our web site).
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USE OF PHENOTYPE
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The system will prioritize the genes according to the description
of the phenotype and its precomputed associations to gene features
as extracted from the literature and the Entrez Gene database.
The input consists simply of the region where the user is looking
for the mutation, and the phenotype of interest, given as an
OMIM identifier. For example, suppose that you are interested
in candidate genes to Hirschsprung disease in 22q13.2. You have
to enter in the appropriate boxes a MIM number that defines
the disease and the target region. In this particular case,
you would enter 131224 (the ID of the OMIM entry for Hirschsprung
disease) in the PHENOTYPE BOX, and q13.2 in the LOCATION BOX.
You would also select the chromosome, 22 and ‘Bands’
in the LOCATION BOX as you have entered the genomic region in
the form of a cytogenetic band. You can refer to our web tutorial
for more input and output options. The output is an ordered
list of candidate genes, both known and predicted, ordered according
to their susceptibility for producing the phenotype. For each
candidate, you can explore any overlapping with ESTs and pseudogenes,
as well as trace back the reasoning the system followed to associate
the candidates to the disease.
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USE OF KNOWN GENES
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This method can be used when one or more genes are already known
or are suspected to be related to the disease. In order to use
it, you have to provide your target region in the LOCATION BOX
in the same way as for the phenotype method, plus one or several
human genes associated to the disease or related mouse genes.
Following with our example, Hirschsprung disease is suspected
to be a multigenic disease, and it is known that mutations in
the endothelin-B receptor (ETRB) gene cause one of the variants
of this disease. In order to find similar or functionally related
genes to ETRB in 22q13.2, you could input the Entrez Gene IDs
of human and mouse ETRBs in the KNOWN GENES BOX. These are 2861
and 13 618, respectively. Like for the phenotype method, the
output is an ordered list of candidate genes that you may explore
in a similar manner. Candidates that overlap with genes that
have (STRING) interactions with any of the known genes input
by the user are flagged.
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USE OF STRING PROTEIN–PROTEIN INTERACTIONS
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The third method for finding candidate genes for multiple gene
phenotypes relies on protein–protein functional interactions,
either known or predicted. The rational is that mutations on
two proteins that participate on the same pathway, or are directly
interacting, will produce the same or very similar phenotypes.
The input for this method consists of the target region, entered
in the LOCATION BOX like in the previous two methods, and a
second region where the phenotype of interest has been also
mapped to. The second region is entered in the SECOND LOCUS
BOX in the same manner, specifying format and chromosome. The
output is a list of genes in the target region that may interact,
according to STRING (
15), with any genes(s) in the SECOND BOX
locus. Candidates are sorted by how likely are their corresponding
interactions to be true indicated by their associated STRING
scores with 0.99 as maximum value. As an illustration, suppose
that you are interested in candidates for myocardial infarction
susceptibility (MIS) on 1p36-p34, one of the loci where the
condition has been linked to (
17). The OMIM entry for MIS (608446
[OMIM]
)
shows that it is linked as well to regions 1q25, 6q25.1 and
1p22.1. By entering 1q25 as the second locus, the output page
displays 20 candidates corresponding to proteins in 1p36-p34
that interact, according to the STRING database, with at least
one protein in 1q25. The first candidate (see
Figure 1) is the
tumor necrosis factor receptor superfamily, member 4 TNFRSF4
(Entrez Gene ID 7293, located on 1p36). This gene comes out
as the top candidate because of its direct interaction (STRING
score = 0.999) with TNFSF4, the tumor necrosis factor (ligand)
superfamily, member 4 (tax-transcriptionally activated glycoprotein
1, 34 kDa) (Entrez Gene ID 7292). The latter has been recently
associated to atherosclerosis susceptibility increasing the
risk for myocardial infarction (
18) making TNFRSF4 a very good
candidate. TNFRSF4 is therefore a quite attractive candidate
from a biological point of view, not yet tested but within the
linkage peak identified by the group that linked MIS to 1p36-p34
(E.J. Topol, personal communication).
View larger version (58K):
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Figure 1. G2D web server interface used to detect genes associated to MIS using the protein-protein method. Top left: the user inputs the coordinates for Locus 1 (1p36-p34) and Locus 2 (1q25) that have been genetically linked to MIS. Top right: G2D displays 20 gene candidates in Locus 1 that code for proteins that interact with proteins encoded by genes found in Locus 2. Bottom: tumor necrosis factor TNFSF4 and its receptor TNFRSF4 are encoded in Locus 2 and Locus 1, respectively, being therefore good candidates; a similar pair of genes (TNFSF8/TNFRSF8) appears as candidate 3.
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CONCLUSION
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The G2D web server allows users to prioritize candidate genes
for practically any disease phenotype in OMIM through three
different methods. The results of benchmarking the methods on
227 diseases that have been shown to be caused by more than
one gene, are given in detail in our web server (see Supplementary
section at our web site), and they can serve as a guidance on
the expected performance of each method. The phenotype and the
known-genes methods show a similar performance prioritizing
the responsible gene in average among the top 25 and 27% of
all genes in the band, respectively, when considering target
bands of 30 MB (averaging a content of 300 genes). It must be
taken into account, however, that more input information about
the molecular cause of the disease is required by the known-genes
method. The protein–protein interactions method has a
very low recall but shows high precision when the STRING scores
associated to the candidates are ‘high’ and ‘very
high’ (0.9 score value and higher). This method could
be applied to a proportion of one in four cases, and in such
situations the responsible gene was among the top 10 candidates,
70% of the times for bands of size 30 MB.
Running times for the algorithms vary from very few seconds to almost immediate yielding of results. Users can examine, for all three procedures, the rational used by the system to make the prioritization, keeping the process transparent. We support this by including extensive hyperlinks to related resources such as NCBI sequence databases, Gene Ontology and the UCSC Genome Browser.
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SUPPLEMENTARY DATA
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Supplementary Data are available at G2D web site.
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ACKNOWLEDEGMENTS
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We thank Christian von Mering for his assistance with STRING
data. This work was supported by funding from the Canadian Institutes
of Health Research, the Ontario Research Development Challenge
Fund, the Canadian Foundation for Innovation, and the Canada
Research Chair Program. Funding to pay the Open Acess publication
charges for this article was provided by CIHR grant MOP-77640.
Conflict of interest statement. None declared.
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ABSTRACT
BACKGROUND