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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
A rapid association test procedure robust under different genetic models accounting for population stratification
Wenan Chen1, Xiangning Chen, Kellie J Archer
1Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298-0032, USA.
Human Heredity
|April 11, 2013
Summary
This study introduces a new robust association test for genome-wide association studies (GWAS) that improves power for recessive and dominant genetic models. The method maintains high power for additive and multiplicative models, enhancing genetic discovery in stratified populations.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
- Population stratification is a common challenge in GWAS, often addressed by methods like the generalized Armitage (GA) trend test using principal component analysis.
- The GA trend test demonstrates high power for additive/multiplicative models but can be less powerful for recessive/dominant models.
Purpose of the Study:
- To develop a novel association test procedure for GWAS that surpasses the power of the GA trend test for recessive and dominant disease models.
- To maintain the power of the GA trend test for additive and multiplicative disease models.
- To provide a robust method for analyzing case-control GWAS data with population stratification.
Main Methods:
- Extension of the Hardy-Weinberg disequilibrium (HWD) trend test to accommodate population stratification.
- Integration of the extended HWD trend test into the GA trend test to create a robust association test procedure.
- Utilizing principal component analysis for population stratification correction within the framework.
Main Results:
- Simulation studies demonstrated the increased power of the proposed method under recessive and dominant models compared to the GA trend test.
- The method maintained comparable power to the GA trend test under additive and multiplicative models.
- Application to a real GWAS dataset confirmed the method's utility and effectiveness.
Conclusions:
- The proposed robust association test effectively addresses the limitations of the GA trend test for certain genetic models.
- This method offers enhanced power for detecting genetic associations in stratified GWAS data, particularly for recessive and dominant effects.
- The findings suggest a valuable new tool for genetic research, improving the ability to discover disease-related genetic variants.
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