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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with
Julie Bertrand1, Maria De Iorio, David J Balding
1aUniversity College London Genetics Institute bUniversity College London, Statistical Science Department, London, UK.
Pharmacogenetics and Genomics
|March 10, 2015
Summary
This study introduces an integrated approach for pharmacokinetic (PK) modeling, simultaneously estimating genetic effects and PK parameters. This method improves precision and reduces study design sensitivity, enhancing genetic variant analysis in drug development.
Area of Science:
- Pharmacokinetics and Pharmacogenomics
- Computational Biology and Bioinformatics
Background:
- Previous penalized regression methods efficiently incorporated genetic variants into pharmacokinetic (PK) models.
- However, phenotypes (individual model parameter estimates) were sensitive to study design.
Purpose of the Study:
- To develop an integrated approach for simultaneous estimation of genetic effect sizes and PK model parameters.
- To improve estimate precision and reduce sensitivity to study design in pharmacogenetic analyses.
Main Methods:
- Simulated 200 datasets under null and alternative scenarios for PK phase II studies.
- Scenarios included varying participant numbers, sampling times, and genetic variants affecting PK parameters.
- Evaluated an integrated approach against a stepwise procedure.
Main Results:
- The integrated approach consistently detected fewer false positives across all scenarios.
- Performance varied by scenario: stepwise outperformed integrated in one, while integrated excelled in another.
- Integrated approach successfully identified genetic variants affecting multiple PK parameters.
Conclusions:
- Standard PK phase II studies with limited data (N=300) may lack power to detect genetic effects on PK.
- The proposed integrated approach effectively combines phase II and clinical routine data.
- This method can identify genetic variants influencing multiple PK parameters, advancing personalized medicine.
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