FitSNPs: A Tool for Identifying Disease-Associated Genetic Variants
Author Information
Author(s): Chen Rong, Morgan Alex A, Dudley Joel, Deshpande Tarangini, Li Li, Kodama Keiichi, Chiang Annie P, Butte Atul J
Primary Institution: Stanford Center for Biomedical Informatics Research
Hypothesis
Highly differentially expressed genes are more likely to have variants associated with disease.
Conclusion
The study shows that highly differentially expressed genes are more likely to harbor disease-associated DNA variants, and FitSNPs can effectively prioritize candidate SNPs from GWASs.
Supporting Evidence
- 99% of disease-associated genes were differentially expressed in one or more GEO datasets.
- The likelihood of having variants associated with disease was 12 times higher among differentially expressed genes.
- FitSNPs successfully distinguished true disease genes from false positives in multiple GWASs.
- DER values were significantly higher for true disease genes compared to false positives.
Takeaway
If a gene is often turned on or off in diseases, it might have changes that cause those diseases. The researchers made a tool to help find these important genes.
Methodology
The study analyzed human microarray studies from the Gene Expression Omnibus to calculate the differential expression ratio for every gene and compared it with disease-associated variants.
Potential Biases
Potential bias in SNP prioritization methods based on existing functional annotations.
Limitations
The study may not account for all factors influencing gene expression and disease association.
Participant Demographics
The study utilized data from various human microarray studies without specific demographic details.
Statistical Information
P-Value
p<0.0001
Statistical Significance
p<0.0001
Digital Object Identifier (DOI)
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