| Title: | Transfer Learning in Genome-Wide Association Studies with Knockoffs |
| Journal: | Sankhya B |
| Published: | 15 Nov 2022 |
| DOI: | https://doi.org/10.1007/s13571-022-00297-y |
| URL: | https://link.springer.com/content/pdf/10.1007/s13571-022-00297-y.pdf |
| Citations: | 6 (4 in last 2 years) as of 8 Aug 2024 |
Publication 8068
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
This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes. The relevance of this methodology is explored in particular within the context of genome-wide association studies, where it can be helpful to address the pressing need for principled ways to suitably account for, and efficiently learn from the genetic variation associated to diverse ancestries. Finally, we apply these methods to analyze several phenotypes in the UK Biobank data set, demonstrating that transfer learning helps knockoffs discover more associations in the data collected from minority populations, potentially opening the way to the development of more accurate polygenic risk scores.4 Authors
- Shuangning Li
- Zhimei Ren
- Chiara Sabatti
- Matteo Sesia
1 Application
| Application ID | Title |
|---|---|
| 27837 | Statistical Methods for Large Scale Genetic Studies |
Enabling scientific discoveries that improve human health