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A Simple Approximation to Bias in Gene-Environment Interaction Estimates When a Case Might Not Be the Case
Iryna Lobach1, Inyoung Kim2, Alexander Alekseyenko3
1Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.
Frontiers in Genetics
|November 5, 2019
Summary
Ignoring nuisance disease states in genetic studies can bias gene-environment interaction (G×E) estimates. This research provides an approximation to quantify this bias, crucial for accurate genetic association studies of complex diseases.
Area of Science:
- Genetics
- Epidemiology
- Biostatistics
Background:
- Case-control genetic association studies investigate the genetic basis of complex diseases.
- Gene-environment interactions (G×E) explore how genetic predisposition is modified by environmental factors.
- Clinical diagnoses may include patients with a "nuisance" pathologic state, distinct from the disease of interest.
Purpose of the Study:
- To derive an approximation for bias in G×E parameter estimates when a nuisance pathologic state is present but ignored.
- To evaluate the impact of ignoring nuisance states on G×E estimates in genetic association studies.
- To demonstrate the practical application of the derived approximation in Alzheimer's disease research.
Main Methods:
- Development of a mathematical approximation to quantify bias in G×E estimates.
- Extensive simulation studies to assess the accuracy and impact of the approximation.
- Application of the approximation to a real-world dataset from a study on Alzheimer's disease.
Main Results:
- Ignoring a nuisance pathologic state can lead to substantial bias in gene-environment interaction (G×E) estimates.
- The derived approximation accurately quantifies this bias in finite samples.
- The method's applicability is confirmed through its use in Alzheimer's disease genetic studies.
Conclusions:
- Nuisance pathologic states represent a critical, often overlooked, complication in genetic association studies.
- Failure to account for nuisance states can significantly distort findings related to gene-environment interactions.
- The proposed approximation offers a valuable tool for correcting and interpreting G×E estimates in the presence of such complications.
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