Analysis of Alcohol Dependence in Families
Author Information
Author(s): Brian H Reck, Nandita Mukhopadhyay, Hui-Ju Tsai, Daniel E Weeks
Primary Institution: University of Pittsburgh
Hypothesis
Can covariate-based methods improve the detection of linkage for alcohol dependence in family studies?
Conclusion
The study found that covariate-based methods detected linkage signals for alcohol dependence, but there was little agreement among the various methods used.
Supporting Evidence
- Linkage signals were detected on chromosomes 3, 4, 7, 10, and 12.
- The highest peak occurred at the GABRB1 gene.
- Empirical p-values less than 0.001 were found for significant linkage.
- The study compared results from covariate-based methods to traditional linkage analysis.
Takeaway
Researchers looked at families to find genes related to alcohol dependence and used new methods to see if they could find more links than before.
Methodology
The study applied four covariate-based methods to analyze genome scan data on alcohol dependence in families.
Potential Biases
The choice of covariates could introduce confounding effects, potentially reducing the power of the analysis.
Limitations
The covariate selection was heuristic and may not have been optimal for identifying true risk factors.
Participant Demographics
Participants were from the Collaborative Studies on Genetics of Alcoholism families.
Statistical Information
P-Value
<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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