Searching for robust associations with a multi-environment knockoff filter

• 成果类型: Article
• 署名作者: Li, S.; Sesia, M.; Romano, Y.; Candes, E.; Sabatti, C.
• 署名单位: Stanford University; University of Southern California; Technion Israel Institute of Technology
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asab055
• 发表日期: 2022
• 页码: 611629
• 关键词: false discovery rate; Linkage Disequilibrium; Causal Inference; prediction; selection; biobank; blocks; models

摘要

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.