We summarized three p-value weighting schemes. The first focuses on control of FWER while the second and third focus on FDR control. The latter two differ in the method of assigning weights. All three methods seek to identify weights that maximize statistical power.

Problem setting I, involving FWER control, is more tractable analytically than the other two and has been studied more extensively. FWER can be easily expressed in a functional form and identifying the optimal weight is reduced to a maximization problem. Roeder et al. [

17] demonstrated application of this method to a genome-wide association study, illustrating how to combine the prior information and the weighted Bonferroni approach. This idea should be of high relevance, as the advent of modern biology has made extensive information on gene location and biochemical pathways available in the public domain. Effective use of such information may hold the key to success of genome-wide studies.

Problem setting II, controlling FDR while seeking optimal weights, remains unresolved. FDR involves a random term in the dominator, making the optimization problem difficult. Yet, the setting is an important one: FDR is widely accepted in genomic studies as a method for controlling false discovery with greater power than the Bonferroni method. Roquain and van de Wiel’s [

14] novel multi-weight approach under fixed rejection region reduces the problem to one similar to setting I, suggesting that some results from the weighted Bonferroni method might be adopted for multi-weight FDR control as well.

The drawback of the multi-weighting approach is that the conditions required to achieve maximum power are stringent and hard to achieve in real situations. Our third problem setting, therefore, attempts to bring more robustness into the weighting scheme by using the stratified FDR method of Sun et al. [2009]. This method has more power than traditional FDR when the distribution of the true signal across stratums is highly skewed. Restricting the weight into *k* values controls FDR in a similar fashion as SFDR but has more flexibility. We view it as a complement between the relative conservative SFDR and the highly dynamic multi-weight approach. It also brings in a way to incorporate prior knowledge on grouping, such as genes from the same biological pathway or SNPs that are located adjacent to one other on the chromosome. This problem setting is the least well-developed of the three, but results from the other two are generally applicable. Therefore, we expect that SFDR will be a major near-term focus of research in weighted FDR.

In conclusion, we recommend setting III as the generally preferred approach for weighted hypothesis testing in genome-wide association studies. While setting I may be easier to implement, it is likely to be too conservative. Setting II controls FDR, which is a more relaxed requirement than setting I. However, it is difficult to identify optimal weights for FDR control. Roquain’s method requires many regulatory conditions that are hard to achieve in real situations. Setting III can incorporate prior knowledge on grouping as well as stabilize the dynamic weighting scheme by offering the same weight within groups. Therefore, we see this as a highly promising approach.

Future work should address the issue of dependence among hypothesis tests. In the context of genome-wide association studies, there may be strong correlations among different SNPs that violate the independence assumption of the BH and Bonferoni procedures. There are at least two approaches to addressing this problem: (1) principle component based methods [

18-

20], which focus on identifying the effective number of tests using matrix decomposition, and (2) permutation test methods [

21-

23], which use efficient algorithms that fully account for the correlation structure among SNPs. Both of these approaches indicate that adjusting for positive dependence typically results in a gain in power. We expect that these approaches can be naturally extended to weighted hypothesis testing to improve the procedures reviewed here.