Using Co-Expression Across Species to Prioritize Disease Genes
Author Information
Author(s): Oti Martin, van Reeuwijk Jeroen, Huynen Martijn A, Brunner Han G
Primary Institution: Radboud University Nijmegen Medical Centre
Hypothesis
Can co-expression data from multiple species improve the prioritization of disease genes compared to using human-only co-expression data?
Conclusion
Evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone.
Supporting Evidence
- Using co-expression data from multiple species improves the predictive value of gene function.
- The study found that dataset quality is more important than quantity for prioritizing disease genes.
- Evolutionary conservation of co-expression enhances the reliability of identified co-expression relationships.
Takeaway
Scientists found that looking at how genes work together in different species helps to find genes that cause diseases better than just looking at humans.
Methodology
The study analyzed co-expression data from five species and compared the performance of disease gene prioritization using evolutionary conservation.
Potential Biases
The study may be limited by the quality of the datasets used and the potential for biases in gene expression data.
Limitations
The performance of co-expression data can vary significantly depending on the datasets used, and larger datasets do not always lead to better prioritization.
Participant Demographics
The study focused on known disease genes from genetic diseases with at least two known causative genes.
Statistical Information
P-Value
p < 10-16
Statistical Significance
p < 10-6
Digital Object Identifier (DOI)
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