ArticleBiometrika2022
Searching for robust associations with a multi-environment knockoff filter.
Article in Biometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.Genome biology · 2026Article
- Diagnosing scientific replicability through probabilistic distinguishability.Bioinformatics (Oxford, England) · 2026Article
- Article
- Controlling FDR in selecting group-level simultaneous signals from multiple data sources with application to the National COVID Collaborative Cohort data.Journal of applied statistics · 2026Article
- Variable Selection with FDR Control for Noisy Data - An Application to Screening Metabolites that Are Associated with Breast Cancer and Colorectal Cancer.Journal of data science : JDS · 2025Article
- Catch me if you can: signal localization with knockoffJournal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- VI-VS: calibrated identification of feature dependencies in single-cell multiomics.Genome biology · 2024Article
- Transfer Learning in Genome-Wide Association Studies with Knockoffs.Sankhya. Series B (2008) · 2022Article
- Interpretable machine learning for genomics.Human genetics · 2022Article
- False discovery rate control in genome-wide association studies with population structure.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across environments, controlling the false discovery rate. The motivation for this problem is that large data sets 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, consistency sometimes provably leads to valid causal inferences even if conditional associations do not. While the proposed method is widely applicable, this paper highlights 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 the UK Biobank data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.