Identifying Genetic Influences on Complex Diseases
Author Information
Author(s): Benjamin J Keller, Richard C McEachin
Primary Institution: Eastern Michigan University
Hypothesis
Can we discover relationships among genes from interacting regions that explain their role in complex disease phenotypes?
Conclusion
The PDG-ACE approach successfully identifies previously published gene relationships and is robust to differences in keyword vocabulary.
Supporting Evidence
- The PDG-ACE algorithm found significant commonality between the CDKN2A/CDKN2B locus and three other T2D candidate genes.
- Validation experiments showed that findings from PDG-ACE are consistent with prior evidence of gene-gene interactions.
- The approach successfully identified previously published relationships while avoiding false positives for non-existent relationships.
Takeaway
This study created a tool to find connections between genes that might cause diseases by looking for common words in their descriptions.
Methodology
The study used a heuristic algorithm called PDG-ACE to mine biomedical keywords from gene descriptions to find relationships among genes.
Potential Biases
There may be a bias in the gene descriptions used, favoring well-studied diseases.
Limitations
The method relies on existing gene descriptions, which may be biased towards well-funded diseases, and it does not assess the context of keywords, potentially increasing false positives.
Statistical Information
P-Value
0.014
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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