Article

G2D: a tool for mining genes associated with disease

Ontario Genomics Innovation Centre, Ottawa Health Research Institute, ON K1H 8L6, Ottawa, Canada.
BMC Genetics (Impact Factor: 2.36). 02/2005; 6:45. DOI: 10.1186/1471-2156-6-45
Source: PubMed

ABSTRACT Human inherited diseases can be associated by genetic linkage with one or more genomic regions. The availability of the complete sequence of the human genome allows examining those locations for an associated gene. We previously developed an algorithm to prioritize genes on a chromosomal region according to their possible relation to an inherited disease using a combination of data mining on biomedical databases and gene sequence analysis.
We have implemented this method as a web application in our site G2D (Genes to Diseases). It allows users to inspect any region of the human genome to find candidate genes related to a genetic disease of their interest. In addition, the G2D server includes pre-computed analyses of candidate genes for 552 linked monogenic diseases without an associated gene, and the analysis of 18 asthma loci.
G2D can be publicly accessed at http://www.ogic.ca/projects/g2d_2/.

Download full-text

Full-text

Available from: Miguel Andrade, Jul 04, 2015
1 Follower
 · 
114 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.
    Genomics 01/2012; 99(1):1-9. DOI:10.1016/j.ygeno.2011.10.002 · 2.79 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The identification of genomic loci associated with human genetic syndromes has been significantly facilitated through the generation of high density SNP arrays. However, optimal selection of candidate genes from within such loci is still a tedious labor-intensive bottleneck. Syndrome to Gene (S2G) is based on novel algorithms which allow an efficient search for candidate genes in a genomic locus, using known genes whose defects cause phenotypically similar syndromes. S2G (http://fohs.bgu.ac.il/s2g/index.html) includes two components: a phenotype Online Mendelian Inheritance in Man (OMIM)-based search engine that alleviates many of the problems in the existing OMIM search engine (negation phrases, overlapping terms, etc.). The second component is a gene prioritizing engine that uses a novel algorithm to integrate information from 18 databases. When the detailed phenotype of a syndrome is inserted to the web-based software, S2G offers a complete improved search of the OMIM database for similar syndromes. The software then prioritizes a list of genes from within a genomic locus, based on their association with genes whose defects are known to underlie similar clinical syndromes. We demonstrate that in all 30 cases of novel disease genes identified in the past year, the disease gene was within the top 20% of candidate genes predicted by S2G, and in most cases--within the top 10%. Thus, S2G provides clinicians with an efficient tool for diagnosis and researchers with a candidate gene prediction tool based on phenotypic data and a wide range of gene data resources. S2G can also serve in studies of polygenic diseases, and in finding interacting molecules for any gene of choice.
    Human Mutation 03/2010; 31(3):229-36. DOI:10.1002/humu.21171 · 5.05 Impact Factor
  • Source