| Title: | Empowering genome-wide association studies via a visualizable test based on the regional association score |
| Journal: | Proceedings of the National Academy of Sciences of the United States of America |
| Published: | 25 Feb 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39999171/ |
| DOI: | https://doi.org/10.1073/pnas.2419721122 |
| URL: | https://pnas.org/doi/pdf/10.1073/pnas.2419721122 |
Publication 15916
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
The genome-wide association studies identified genes associated with many diseases, but the identification and verification of disease variants are still challenging due to small effects and large number of individual variants. In this paper, we propose a powerful method that first quantifies the strength of regional associations at each single nucleotide polymorphism and converts these measures into time series data before using a change point detection algorithm to identify significant regions. In our extensive simulation study, the proposed method consistently demonstrates greater power than existing alternatives, achieving a relative increase of over 20% in challenging scenarios where true causal variants are sparse and multiple association regions exist at the same time, while maintaining a lower false positive rate.</p>
7 Keywords
- Algorithms
- Computer Simulation
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Polymorphism, Single Nucleotide
2 Authors
- Yiran Jiang
- Heping Zhang
1 Application
| Application ID | Title |
|---|---|
| 42009 | Genetic study of complex phenotypes |
Enabling scientific discoveries that improve human health