ArticleNature communications2019
Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review.International journal of molecular sciences · 2022Pooled it
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- A deep dive into statistical modeling of RNA splicing QTLs reveals variants that explain neurodegenerative disease.American journal of human genetics · 2025Article
- Statistical Methods for Multi-Omics Analysis in Neurodevelopmental Disorders: From High Dimensionality to Mechanistic Insight.Biomolecules · 2025Review
- Neighbourhood out-of-home food environment, menu healthiness, and their associations with meal purchasing and diet quality: a multiverse analysis.Nutrition journal · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.Scientific reports · 2024Article
- VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.Communications biology · 2023Article
- Measuring impact of New Mexico prekindergarten on standardized test scores and high school graduation using propensity score matching.International journal of child care and education policy · 2023Article
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- Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.Frontiers in neuroimaging · 2023Article
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- Covariate adaptive familywise error rate control for genome-wide association studies.Biometrika · 2021Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction.PloS one · 2021Article
- Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis.Disease markers · 2021Article
- Transcriptome analysis in mice treated with vigabatrin identifies dysregulation of genes associated with retinal signaling circuitry.Epilepsy research · 2020Article
- A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Confidence maps: statistical inference of cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multiple hypothesis testing is an essential component of modern data science. In many settings, in addition to the p-value, additional covariates for each hypothesis are available, e.g., functional annotation of variants in genome-wide association studies. Such information is ignored by popular multiple testing approaches such as the Benjamini-Hochberg procedure (BH). Here we introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% more associations than BH at the same false discovery rate. We prove that AdaFDR controls false discovery proportion and show that it makes substantially more discoveries while controlling false discovery rate (FDR) in extensive experiments. AdaFDR is computationally efficient and allows multi-dimensional covariates with both numeric and categorical values, making it broadly useful across many applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.