Ultimately, we can expect large-scale, high-depth, genome-wide sequencing to enable the systematic exploration of the entire allele-frequency, effect-size space and provide empirical resolution of many of these issues. However, there remain serious financial, logistical and analytical barriers to the implementation of this technology, and the number of such experiments that could be supported by the major funders is, for the time being, limited.
All this means that, for the next few years, the power of next-generation sequencing will need to be used carefully if a profusion of underpowered discovery efforts is to be avoided. Efforts targeted to specific genomic regions (around particular candidate genes or pathways or exons across the genome, for example) are attractive because high coverage of the selected areas in large sample sizes can be generated at reasonable cost. Whole-genome sequencing will, for now, be restricted to low-pass coverage across respectable sample sizes, or high-depth coverage in smaller, highly selected, phenotypically extreme sample sets.
The genomic distribution of disease-effect loci will have a major impact on the success of these alternative approaches (Figure ). If the low-frequency and rare variants influencing a given trait are disproportionately located in the same loci as the common variants that have been found to date, then targeted follow-up of regions revealed by GWA studies will be a powerful approach, and extending the range and scope of GWA analysis (to other ethnic groups, for example) should be a particularly efficient strategy. If, on the other hand, the 'dark matter' variants have little positional (or biological) overlap with those already known, then genome-wide resequencing is likely to be the only practical way to find them. The evidence so far (overlap between monogenic and multifactorial loci; growing numbers of loci with multiple independent association signals; extensive pleiotropy, and so on [23
]) provides some support for the former view. Effort in tracking down common susceptibility variants, as well as being valuable in its own right, should therefore guide researchers towards other types of causal variants.
Figure 1 Causal variant signals and their genomic distribution. Two possible versions of the state of nature are presented (see text). In one ('even'), causal variants differing in terms of allele frequency (color scale) and effect size (height of bar) are distributed (more ...)