MegaSNPHunter: A Learning Approach to Detect Disease Predisposition SNPs
Author Information
Author(s): Wan Xiang, Yang Can, Yang Qiang, Xue Hong, Tang Nelson LS, Yu Weichuan
Primary Institution: Hong Kong University of Science and Technology
Hypothesis
The interactions of multiple single nucleotide polymorphisms (SNPs) are hypothesized to affect an individual's susceptibility to complex diseases.
Conclusion
MegaSNPHunter outperforms existing methods in identifying disease-associated SNP interactions from genome-wide studies.
Supporting Evidence
- MegaSNPHunter identified 7 significant SNP interactions in a study on Parkinson disease.
- The method outperformed BEAM in detecting interactions in both synthetic and real data sets.
- MegaSNPHunter is the first approach capable of identifying disease-associated SNP interactions from WTCCC studies.
Takeaway
MegaSNPHunter is a tool that helps scientists find how different genetic variations work together to affect health, especially for complex diseases.
Methodology
MegaSNPHunter uses a hierarchical learning approach to rank multi-SNP interactions from case-control genotype data.
Limitations
The method may miss interactions between SNPs that are not located in the same subgenome.
Participant Demographics
The study included 1999 cases and 1504 controls for rheumatoid arthritis and 541 samples for Parkinson's disease.
Statistical Information
P-Value
6.83 * 10-15
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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