Knockoffs with side information
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 2
Abstract
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can allow the statistician to pay attention to variables with a greater potential which, in turn, may lead to more discoveries. We introduce an adaptive knockoff filter, which generalizes the knockoff procedure (Ann. Statist. 43 (2015) 2055–2085; J. R. Stat. Soc. Ser. B. Stat. Methodol. 80 (2018) 551–577), in that it uses both the data at hand and side information to adaptively order the variables under study and focus on those that are most promising. The adaptive knockoffs procedure controls the finite-sample false discovery rate (FDR), and we demonstrate its power by comparing it with other structured multiple testing methods. We also apply our methodology to real genetic data in order to find associations between genetic variants and various phenotypes such as Crohn’s disease and lipid levels. Here, the adaptive knockoffs method makes more discoveries than reported in previous studies on the same datasets.
Keywords
Bayesian two-group models, false discovery rate (FDR), Genome-wide association study (GWAS), knockoff filters, multiple testing, Variable selection
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