Multiple hypothesis testing is a critical part of modern bioinformatic analysis. When testing for significant changes between conditions on many thousands of genes, for instance in an RNASeq experiment, the goal is maximize the number of discoveries while controlling the false discoveries.
Typically, this is done by using the BenjaminiHochberg (BH) procedure, which aims to adjust pvalues so that no more than a set fraction (usually 5%) of discoveries are false positives (FDR = 0.05). The BH method is better powered and less stringent than the more strict familywise error rate (FWER) control, and therefore more appropriate to modern genomics experiments that make thousands of simultaneous comparisons. However, the BH method is still limited by the fact that it uses only pvalues to control the FDR, while treating each test as equally powered.
A new method, Independent Hypothesis Weighting (IHW), aims to take advantage of the fact that individual tests may differ in their statistical properties, such as sample size, true effect size, signaltonoise ratio, or prior probability of being false. For example, in an RNASeq experiment, highlyexpressed genes may have better signaltonoise than lowexpressed genes.
The IHW method applies weights (a nonnegative number between zero and one) to each test in a datadriven way. The input to the method is a vector of pvalues (just like BH/FDR) and a vector of continuous or categorical covariates (i.e., any data about each test that is assumed to be independent of the test pvalue under the null hypothesis).
From the paper linked above, Table 1 lists possible covariates:
Application  Covariate 



Differential expression analysis  Sum of read counts per gene across all samples [12] 
Genomewide association study (GWAS)  Minor allele frequency 
ExpressionQTL analysis  Distance between the genetic variant and genomic location of the phenotype 
ChIPQTL analysis  Comembership in a topologically associated domain [16] 
ttest  Overall variance [9] 
Twosided tests  Sign of the effect 
Various applications  Signal quality, sample size 
In simplified form, the IHW method takes the tests and groups them based on the supplied covariate. It then calculates the number of discoveries (rejections of the null hypothesis) using a set of weights. The weights are iterated until the method converges on the optimal weights for each covariatebased group that maximize the overall discoveries. Additional procedures are employed to prevent overfitting of the data and to make the procedure scale easily to millions of comparisons.
The authors of the method claim that IHW is better powered than BH for making empirical discoveries when working with genomic data. It can be accessed from within Bioconductor.