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This document presents a novel regression framework for estimating false discovery rates (FDR) conditional on covariates, particularly in the context of multiple hypothesis testing in scientific studies. The authors apply their method to a genome-wide association meta-analysis for body mass index, demonstrating how covariates like sample sizes and allele frequencies can improve FDR estimation. The proposed approach is implemented in the Bioconductor package swfdr and is evaluated through simulations and com

Author

Snoopy

Language

EN