This method is likely to prove most useful in complex biomedical phenotypes where information exists about plausible pathophysiological mechanisms. Particularly when applied to genome wide data, the task will be to develop informed sets of candidate pathways that can be tested as plausible a priori hypotheses. Defining the boundaries of individual pathways may prove challenging as biological pathways are rarely discrete. Nonetheless, if the CDCV hypothesis is correct, genetic variation for many common diseases may be scattered throughout a number of genes within particular biological pathways. Testing a limited number of such pre-defined pathways is likely to lead to a substantial gain in statistical power if it is possible to nominate the likely candidate pathways. Such a gain in power could significantly reduce the need for very large samples for GWAS needed to compensate for the multiple testing burden.
