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The recent proliferation of medical data, such as genetics and electronichealth records (EHR), offers new opportunities to find novel predictors ofhealth outcomes. Presented with a large set of candidate features, interestoften lies in selecting the ones most likely to be predictive of an outcome forfurther study such that the goal is to control the false discovery rate (FDR)at a specified level. Knockoff filtering is an innovative strategy forFDR-controlled feature selection. But, existing knockoff methods make strongdistributional assumptions that hinder their applicability to real world data.We propose Bayesian models for generating high quality knockoff copies thatutilize available knowledge about the data structure, thus improving theresolution of prognostic features. Applications to two feature sets areconsidered: those with categorical and/or continuous variables possibly havinga population substructure, such as in EHR; and those with microbiome featureshaving a compositional constraint and phylogenetic relatedness. Throughsimulations and real data applications, these methods are shown to identifyimportant features with good FDR control and power.

Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure