Detecting Gene Interactions in Genetic Studies
Author Information
Author(s): Amina Barhdadi, Marie-Pierre Dubé
Primary Institution: Université de Montréal
Hypothesis
Can two-stage strategies improve the detection of gene × gene interactions in genetic association studies?
Conclusion
The study found that using only additive genetic effects in modeling can effectively capture most underlying interactions while reducing the burden of multiple testing.
Supporting Evidence
- The simultaneous method optimizes the search for interaction effects between SNPs that provide some indication of marginal associations.
- A large portion of the interactions detected were between SNPs within 1000 kb of each other.
- The conditional design can capture interactions with loci that have no marginal effects.
Takeaway
Researchers looked at how genes work together to affect diseases and found that testing pairs of genes in two steps can help find important interactions without too much extra work.
Methodology
The study used logistic regression models to test SNP interactions in a two-stage approach with a sample of cases and controls.
Potential Biases
Potential for false positives due to high marginal signals in some SNPs.
Limitations
The analysis was limited to only the first replicate of the simulated data due to computational constraints.
Participant Demographics
1500 cases with rheumatoid arthritis and 2000 controls.
Statistical Information
P-Value
<10-200
Statistical Significance
p<0.05
Want to read the original?
Access the complete publication on the publisher's website