Detecting Susceptibility Genes in Complex Diseases
Author Information
Author(s): Kim Sung, Zhang Kui, Sun Fengzhu
Primary Institution: University of Southern California
Hypothesis
Can a set association method effectively identify genetic variations responsible for complex diseases involving multiple genes?
Conclusion
The multiallelic set association method did not detect significant markers associated with cholesterol levels in the simulated data.
Supporting Evidence
- Both bi-allelic and multiallelic set association tests have correct type I error rates.
- BSA and MSA can be more powerful than individual marker analysis when multiple genes are involved.
- The study applied MSA to simulated data sets from Genetic Analysis Workshop 13.
Takeaway
The study tried to find genes that cause diseases by looking at many genes at once, but it didn't find any strong links for high cholesterol.
Methodology
The study used a set association method with simulations to analyze genetic data from case-control studies.
Limitations
The method failed to detect significant associations due to wide spacing between markers and lack of association with the phenotype.
Participant Demographics
200 simulated haplotypes with 100 cases and 100 controls.
Statistical Information
P-Value
0.5417
Statistical Significance
p>0.05
Digital Object Identifier (DOI)
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