Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
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Updated: Mar 20, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Nikolaos Ignatiadis1, Bernd Klaus1, Judith B Zaugg1
1European Molecular Biology Laboratory, Heidelberg, Germany.
Abstract:
Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test's power or prior probability of the null hypothesis (http://www.bioconductor.org/packages/IHW). IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.
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