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Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies
Chen Lu1, George T O'Connor, Josée Dupuis
1Department of Biostatistics, Boston University School of Public Health, Boston, Mass., USA.
Human Heredity
|December 22, 2016
Summary
We propose a data-splitting method for meta-analyzing penalized regression in genome-wide association studies. Splitting entire cohorts, rather than data within cohorts, yields more accurate results for multi-cohort analyses.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Penalized regression is effective for genome-wide association studies (GWAS).
- Meta-analysis enhances statistical power and protects patient confidentiality in GWAS.
- Existing meta-analysis methods for penalized regression in multi-cohort settings are limited.
Purpose of the Study:
- To develop and evaluate methods for meta-analyzing penalized regression results across multiple cohorts.
- To compare data-splitting strategies for obtaining valid p-values in multi-cohort meta-analysis.
- To provide a recommended approach for robust meta-analysis of penalized regression in large genetic studies.
Main Methods:
- Proposed a data-splitting approach to generate valid p-values for meta-analysis.
- Investigated two data-splitting strategies: splitting cohorts versus splitting data within cohorts.
- Developed three meta-analysis methods based on p-values and compared them to mega-analysis (pooling individual data).
- Applied proposed methods to Framingham Heart Study data, simulating a multi-cohort scenario.
Main Results:
- Simulations indicated that splitting cohorts outperformed splitting data within cohorts.
- The proposed cohort-splitting meta-analysis method produced results comparable to mega-analysis in real data.
- The data-splitting approach enables valid meta-analysis of penalized regression coefficients and standard errors.
Conclusions:
- Recommends splitting cohorts as the preferred strategy for meta-analysis of penalized regression in multi-cohort settings.
- The proposed methods provide valid p-values essential for accurate meta-analysis.
- This approach enhances the utility of meta-analysis for large-scale genetic studies using penalized regression.
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