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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Robust joint analysis allowing for model uncertainty in two-stage genetic association studies
Dongdong Pan1, Qizhai Li, Ningning Jiang
1Department of Statistics, Yunnan University, Kunming 650091, PR China.
BMC Bioinformatics
|January 8, 2011
Summary
We propose robust joint analyses for two-stage genome-wide association studies (GWASs). The MAX3 joint analysis demonstrates superior performance and is recommended for identifying complex disease susceptibility loci.
Area of Science:
- Genetics
- Statistical genomics
- Bioinformatics
Background:
- Two-stage designs are cost-efficient for genome-wide association studies (GWASs) to find disease susceptibility loci.
- Replication-based analysis in GWASs can lead to efficiency loss.
- Existing joint analyses improve efficiency but may lack robustness if the assumed genetic model is inappropriate.
Purpose of the Study:
- To propose robust joint analysis methods for two-stage GWASs.
- To develop efficient procedures for significance level evaluation and power calculation.
- To evaluate the performance of proposed robust joint analyses.
Main Methods:
- Proposed joint analyses utilizing two robust test statistics: MERT and MAX3.
- Developed computationally efficient procedures for statistical evaluation.
- Investigated performance through extensive simulations and a real genetic dataset.
Main Results:
- The joint analysis based on the MAX3 test statistic exhibited the best overall performance.
- Developed efficient procedures for significance level evaluation and power calculations.
- Simulations and real data analysis confirmed the robustness and efficiency of the proposed methods.
Conclusions:
- The MAX3 joint analysis is the most robust procedure among the evaluated methods for two-stage GWASs.
- Recommends the MAX3 joint analysis for its superior performance in two-stage genome-wide association studies.
- Provides a more reliable approach for identifying genetic loci associated with complex diseases.
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