Catch me if you can: Signal localization with knockoff e-values
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We consider problems where many, somewhat redundant, hypotheses are tested and we are interested in reporting the most precise rejections, with false discovery rate (FDR) control. For example, a common goal in genetics is to identify DNA variants that carry distinct information on a trait of interest. However, strong local dependencies between nearby variants make it challenging to distinguish which of the many correlated features most directly influence the phenotype. A common solution is then to identify sets of variants that cover the truly important ones. Depending on the signal strengths, it is possible to resolve the individual variant contributions with more or less precision. Assuring FDR control on the reported findings with these adaptive searches is, however, often impossible. To design a multiple comparison procedure that allows for an adaptive choice of resolution with FDR control, we leverage e-values and linear programming. We adapt this approach to problems where knockoffs and group knockoffs have been successfully applied to test conditional independence hypotheses. We demonstrate its efficacy by analyzing data from the UK Biobank.
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