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Assessing exposure effects on gene expression
Sarah A Reifeis1, Michael G Hudgens1, Mete Civelek2
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Genetic Epidemiology
|June 9, 2020
Summary
Confounding in genomics can bias exposure effect estimates. Inverse probability weighting (IPW) and the g-formula provide consistent estimates, even with exposure-confounder interactions, unlike standard regression. These methods improve gene expression analysis.
Area of Science:
- Genomics
- Observational Epidemiology
- Statistical Genetics
Background:
- Observational genomics data frequently exhibit confounding, where the relationship between an exposure and gene expression is distorted by other factors.
- Standard regression models adjusting for confounders may fail to provide accurate exposure effect estimates when interactions between the exposure and confounders influence gene expression.
Purpose of the Study:
- To compare the performance of inverse probability weighting (IPW) and the parametric g-formula against traditional regression for estimating exposure effects on gene expression in the presence of confounding and interactions.
- To evaluate the advantages and disadvantages of each method through simulations and a real-world case study.
Main Methods:
- Simulation studies were conducted to compare regression, IPW, and g-formula approaches for estimating exposure effects on gene expression.
- The methods were applied to analyze the effect of current smoking on gene expression in adipose tissue.
- IPW was integrated into a standard genomics data analysis pipeline, while the g-formula involved modifications to regression models.
Main Results:
- Inverse probability weighting (IPW) and the g-formula yield consistent exposure effect estimates, particularly when exposure and confounders interact to affect gene expression.
- Standard regression models may provide biased estimates in the presence of such interactions.
- The study demonstrated the practical application and comparative performance of these statistical methods in a genomics context.
Conclusions:
- IPW and the g-formula are robust alternatives to standard regression for estimating exposure effects on gene expression in observational genomics studies with confounding and interactions.
- These methods offer improved accuracy and consistency, facilitating more reliable biological interpretations from genomic data.
- The findings support the adoption of IPW and g-formula in genomics research for precise effect estimation.
Keywords:
confoundinginverse probability weightingobservational genomicsparametric g-formularegressionMore Related Videos
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