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Predicting Type 1 Diabetes Candidate Genes using Human Protein-Protein Interaction Networks.

Gao S, Wang X - J Comput Sci Syst Biol (2009)

Bottom Line: We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes.Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D.CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics & the Comprehensive Diabetes Center, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL 35294, USA.

ABSTRACT
BACKGROUND: Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases. RESULTS: In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates. CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.

No MeSH data available.


Related in: MedlinePlus

The PPI network of known (circle) and predicted disease genes (diamond).
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Figure 4: The PPI network of known (circle) and predicted disease genes (diamond).

Mentions: Using all 222 T1D genes (annotated in HPRD) as baits, we arrived at a list of 68 predicated new candidates, given in table 1. None of these has been previously associated to T1D according to T1Dbase. Figure 4 depicts the interactions between all known and predicted T1D genes.


Predicting Type 1 Diabetes Candidate Genes using Human Protein-Protein Interaction Networks.

Gao S, Wang X - J Comput Sci Syst Biol (2009)

The PPI network of known (circle) and predicted disease genes (diamond).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2818071&req=5

Figure 4: The PPI network of known (circle) and predicted disease genes (diamond).
Mentions: Using all 222 T1D genes (annotated in HPRD) as baits, we arrived at a list of 68 predicated new candidates, given in table 1. None of these has been previously associated to T1D according to T1Dbase. Figure 4 depicts the interactions between all known and predicted T1D genes.

Bottom Line: We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes.Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D.CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics & the Comprehensive Diabetes Center, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL 35294, USA.

ABSTRACT
BACKGROUND: Proteins directly interacting with each other tend to have similar functions and be involved in the same cellular processes. Mutations in genes that code for them often lead to the same family of disease phenotypes. Efforts have been made to prioritize positional candidate genes for complex diseases utilize the protein-protein interaction (PPI) information. But such an approach is often considered too general to be practically useful for specific diseases. RESULTS: In this study we investigate the efficacy of this approach in type 1 diabetes (T1D). 266 known disease genes, and 983 positional candidate genes from the 18 established linkage loci of T1D, are compiled from the T1Dbase (http://t1dbase.org). We found that the PPI network of known T1D genes has distinct topological features from others, with significantly higher number of interactions among themselves even after adjusting for their high network degrees (p<1e-5). We then define those positional candidates that are first degree PPI neighbours of the 266 known disease genes to be new candidate disease genes. This leads to a list of 68 genes for further study. Cross validation using the known disease genes as benchmark reveals that the enrichment is ~17.1 fold over random selection, and ~4 fold better than using the linkage information alone. We find that the citations of the new candidates in T1D-related publications are significantly (p<1e-7) more than random, even after excluding the co-citation with the known disease genes; they are significantly over-represented (p<1e-10) in the top 30 GO terms shared by known disease genes. Furthermore, sequence analysis reveals that they contain significantly (p<0.0004) more protein domains that are known to be relevant to T1D. These findings provide indirect validation of the newly predicted candidates. CONCLUSION: Our study demonstrates the potential of the PPI information in prioritizing positional candidate genes for T1D.

No MeSH data available.


Related in: MedlinePlus