| Title: | PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects |
| Journal: | BMC Bioinformatics |
| Published: | 12 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38216869/ |
| DOI: | https://doi.org/10.1186/s12859-023-05627-z |
| URL: | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-023-05627-z |
Publication 8952
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundMeta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles.ResultsWe found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry.ConclusionsOur findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY.</p>
4 Authors
- Emma E. Kim
- Chloe Soohyun Jang
- Hakin Kim
- Buhm Han
1 Application
| Application ID | Title |
|---|---|
| 46263 | Development of cross-phenotype meta-analysis methods that can account for effect size heterogeneity between diseases and sample overlap |
Enabling scientific discoveries that improve human health