New paper: Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

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: 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.

Read more