| Title: | Searching for robust associations with a multi-environment knockoff filter |
| Journal: | Biometrika |
| Published: | 2 Nov 2021 |
| DOI: | https://doi.org/10.1093/biomet/asab055 |
Publication 10494
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
Summary In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting. In fact, sometimes consistency provably leads to valid causal inferences even if conditional associations do not. Although the proposed method is widely applicable, in this paper we highlight its relevance to genome-wide association studies, in which robustness across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to UK Biobank data.</p>
5 Authors
- S Li
- M Sesia
- Y Romano
- E Candès
- C Sabatti
1 Application
| Application ID | Title |
|---|---|
| 27837 | Statistical Methods for Large Scale Genetic Studies |
Enabling scientific discoveries that improve human health