Comparing Continuous and Discrete Environments in Genetic Studies
Author Information
Author(s): Kevin R Viel, Diane M Warren, Alfonso Buil, Thomas D Dyer, Tom E Howard, Laura Almasy
Primary Institution: Emory University
Hypothesis
Does using a continuous variable for environmental factors provide better insights into gene-environment interactions than discrete variables?
Conclusion
Using a continuous environment in genetic analyses may yield more significant results and better model the relationship between genetics and environmental factors.
Supporting Evidence
- The continuous environment produced more significant gene-environment interactions.
- Lower Akaike's Information Criterion (AIC) values were observed with continuous parameterization.
- Genetic variance increased with higher cigarette pack-years.
Takeaway
This study looked at how using continuous data, like the number of cigarettes smoked, can help scientists understand how genes and the environment work together. It found that continuous data often gives better results than just saying someone is a smoker or not.
Methodology
The study used variance components models to analyze gene-environment interactions in the COGA dataset, comparing discrete and continuous environmental variables.
Limitations
The study's findings may not apply universally, as the effectiveness of parameterization can depend on the specific traits and environments being studied.
Statistical Information
P-Value
0.022, 0.025
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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