ArticleBioinformatics (Oxford, England)2024
Second-order group knockoffs with applications to genome-wide association studies.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 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
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- It's a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline.bioRxiv : the preprint server for biology · 2025Article
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Abstract
motivationConditional testing via the knockoff framework allows one to identify-among a large number of possible explanatory variables-those that carry unique information about an outcome of interest and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome-wide association studies (GWAS), which have the goal of identifying genetic variants that influence traits of medical relevance.
resultsWhile conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. AVAILABILITY AND IMPLEMENTATION: The described algorithms are implemented in an open-source Julia package Knockoffs.jl. R and Python wrappers are available as knockoffsr and knockoffspy packages.
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