Using Recursive Partitioning to Analyze Genetic Linkage in Alcoholism
Author Information
Author(s): Xu Wei, Taylor Chelsea, Veenstra Justin, Bull Shelley B, Corey Mary, Greenwood Celia M
Primary Institution: Hospital for Sick Children, Toronto, Ontario, Canada
Hypothesis
Can a recursive-partitioning algorithm improve the detection of genetic linkage in alcoholism by identifying covariate interactions?
Conclusion
The recursive-partitioning model successfully detected linkage signals involving covariate interactions that traditional methods could not identify.
Supporting Evidence
- The RP model identified suggestive regions on chromosomes 2, 4, 6, 14, and 20.
- NPL scores showed no linkage evidence, highlighting the RP model's effectiveness.
- The study included 144 affected relative pairs to avoid dependency issues.
Takeaway
Researchers created a new method to find genetic links to alcoholism by looking at how different factors, like age and smoking, affect the results.
Methodology
The study used a recursive-partitioning model to analyze genome-wide microsatellite data from affected relative pairs, incorporating covariates like smoking status and age.
Potential Biases
Potential for false positives due to the nature of data mining algorithms.
Limitations
The method assumes independence of relative pairs, which may not hold true in families with multiple affected pairs.
Participant Demographics
1,614 individuals from 143 pedigrees, primarily affected by alcohol dependence.
Statistical Information
P-Value
0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website