ArticleBiometrika2019
Gene hunting with hidden Markov model knockoffs.
Article in Biometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.American journal of human genetics · 2021Pooled it
- Robust Inference With Ghostknockoffs in Genome-Wide Association Studies With Sample Relatedness.Genetic epidemiology · 2026Article
- CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.Genome biology · 2026Article
- Statistical knockoffs improve biomarker discovery from transcriptomic data.Briefings in bioinformatics · 2026Article
- Transcriptome-based identification of the WRKY gene family in Polygala tenuifolia Willd. and their response to cold stress.BMC plant biology · 2026Article
- 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
- It's a wrap: deriving distinct discoveries with FDR control after a GWAS analysis.Bioinformatics advances · 2026Article
- Robust FDR Control for Neuroimaging-based Classification via Knockoffs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Reflection knockoffs via householder reflection: applications in proteomics and genetic fine mapping.Genetics · 2025Article
- Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations.Genome biology · 2025Article
- It's a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline.bioRxiv : the preprint server for biology · 2025Article
- Genome-Wide Characterization of thePlants (Basel, Switzerland) · 2025Article
- Characterization of DnaJ gene family in Castanea mollissima and functional analysis of CmDnaJ27 under cold and heat stresses.BMC plant biology · 2025Article
- Catch me if you can: signal localization with knockoffJournal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Genome-wide identification and expression profiling ofFrontiers in plant science · 2025Article
- Second-order group knockoffs with applications to genome-wide association studies.Bioinformatics (Oxford, England) · 2024Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- A rigorous and versatile statistical test for correlations between stationary time series.PLoS biology · 2024Article
- KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies.American journal of human genetics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome-wide association studies with guaranteed false discovery rate control. We apply our method to datasets on Crohn's disease and some continuous phenotypes.
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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.