ArticleBiometrical journal. Biometrische Zeitschrift2021
Accurate error control in high-dimensional association testing using conditional false discovery rates.
Article in Biometrical journal. Biometrische Zeitschrift, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency.Clinical immunology (Orlando, Fla.) · 2024Pooled it
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Exploiting pleiotropy to enhance variant discovery with functional false discovery rates.Nature computational science · 2025Article
- Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.Nature communications · 2024Article
- Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism.American journal of respiratory and critical care medicine · 2024Article
- Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context.PLoS genetics · 2023Article
- Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression.Addiction biology · 2023Article
- Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS.BMC bioinformatics · 2022Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- Optimizing expression quantitative trait locus mapping workflows for single-cell studies.Genome biology · 2021Article
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2 authors.
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Abstract
High-dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p-values for the equivalent hypotheses for a second trait. Although related to the Benjamini-Hochberg procedure, it does not permit any easy control of type-1 error rate and existing methods are over-conservative. We propose a new method for type-1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome-wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis.
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