| Title: | Fast Signal Region Detection With Application to Whole Genome Association Studies |
| Journal: | Journal of the American Statistical Association |
| Published: | 7 Apr 2025 |
| DOI: | https://doi.org/10.1080/01621459.2025.2464271 |
Publication 15962
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
Research on the localization of the genetic basis associated with diseases or traits has been widely conducted in the last few decades. Scan methods have been developed for region-based analysis in whole-genome association studies, helping us better understand how genetics influences human diseases or traits, especially when the aggregated effects of multiple causal variants are present. In this paper, we propose a fast and effective algorithm coupling with high-dimensional test for simultaneously detecting multiple signal regions, which is distinct from existing methods using scan or knockoff statistics. The idea is to conduct binary splitting with re-search and arrangement based on a sequence of dynamic critical values to increase detection accuracy and reduce computation. Theoretical and empirical studies demonstrate that our approach enjoys favorable theoretical guarantees with fewer restrictions and exhibits superior numerical performance with faster computation. Utilizing the UK Biobank data to identify the genetic regions related to breast cancer, we confirm previous findings and meanwhile, identify a number of new regions that suggest strong associations with risk of breast cancer and deserve further investigation. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.</p>
3 Authors
- Wei Zhang
- Fan Wang
- Fang Yao
1 Application
| Application ID | Title |
|---|---|
| 79237 | Distribution and correlation free high-dimensional signal region detection with applications to whole genome association studies |
Enabling scientific discoveries that improve human health