Comparison of Methods for Genetic Association Studies
Author Information
Author(s): Ye Zhan, Elizabeth J Atkinson, Brooke L Fridley, Mariza de Andrade
Primary Institution: Michigan Technological University and Mayo Clinic
Hypothesis
How do Bayesian and non-Bayesian methods compare in genetic association studies?
Conclusion
Both regression and Bayesian methods effectively identified target genetic loci in the simulated data.
Supporting Evidence
- The study compared Bayesian and non-Bayesian methods for genetic analysis.
- Regression methods correctly identified target loci in the simulated data.
- Different methods were able to pick the correct SNPs.
- The study used data from the Genetic Analysis Workshop 15.
Takeaway
The study looked at different ways to find genes linked to diseases and found that many methods worked well.
Methodology
The study used various statistical methods including single-marker tests, stepwise regression, random forests, and Bayesian model averaging on simulated genetic data.
Limitations
The methods could not fully exploit their advantages due to the strong signal of causal SNPs and pre-selected criteria.
Participant Demographics
1500 RA cases and 2000 controls, matched for sex.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Want to read the original?
Access the complete publication on the publisher's website